Runs on Money Market Mutual Funds∗

Lawrence Schmidt†

University of California, San DiegoAllan Timmermann‡

University of California, San Diego

Russ Wermers§

University of Maryland at College Park

January 2, 2013

Abstract

This paper studies daily investor flows to and from each money market mutual fund duringthe period surrounding and including the money fund crisis of September 2008. We focus onthe determinants of flows in the prime money fund category to shed light on the covariatesof money fund runs, since this category was, by far, the most heavily impacted by the moneyfund crisis. We find that outflows during the crisis period of September 17-19 are concentratedamong a small fraction of funds having certain characteristics. Institutional investors focusedtheir run-like behavior on large funds that were part of a complex having large amounts ofinstitutional money funds (as a fraction of all money funds). In addition, such investors weremore likely to run from funds with higher yields, lower expense ratios, and higher prior flowvolatility, indicating that “hot money” chased yields, but selectively ran from higher-yield fundsthat were more vulnerable. Our analysis also suggests that prime retail money funds exhibitedmany of the same behaviors as institutional funds, although at a much slower and more drawn-out pace. Our paper provides a framework that is useful in understanding potential furtherregulatory responses to the crisis.

Key words: Money market mutual funds; financial crisis, money fund crisis, breaking thebuck, quantile regression.

JEL codes:

∗We thank participants at Finlawmetrics 2010: Fifth International Conference on Financial Regulation and Su-pervision (Central Banking, Regulation, and Supervision, Bocconi University, Milan, June 2010); the Paul WoolleyCentre 2010 Conference at University of Technology, Sydney (especially Philip Gharghori of Monash University, thediscussant); the CAREFIN Conference (September 2010, Milan); the Conference on Systemic Risk and Data Issuesin Washington, D.C. (September 2011, a joint conference of Carnegie Mellon, NYU, University of California, Berke-ley, and University of Maryland; special thanks to Chester Spatt, the discussant); the University of Notre DameConference on Current Topics in Financial Regulation (June 2012, Washington, D.C.; especially Woodrow Johnson,the discussant); the Western Finance Association Annual Meetings (2012, Las Vegas); Asian Finance AssociationAnnual Meetings (2012, Taipei); Second Conference of the Macroprudential Research Network (European CentralBank, Frankfurt, 2012); FMA Asia Annual Meetings (2012, Phuket); and seminar participants at the University ofIllinois at Chicago. We gratefully acknowledge financial support for this research from the Center for Financial Policyat the Smith School of the University of Maryland and from the Paul Woolley Centre for the Study of Capital MarketDysfunctionality at the University of Technology, Sydney.†Department of Economics, UCSD, 9500 Gilman Drive, La Jolla CA 92093-0508. lschmidt@ucsd.edu‡Rady School of Management, University of California, San Diego, 9500 Gilman Drive, La Jolla CA 92093-0553.

atimmermann@ucsd.edu.§Smith School of Business, University of Maryland, College Park, MD 20850. rwermers@rhsmith.umd.edu.

1. Introduction

Bank runs have long been a subject of academic and regulatory interest. There are two primary

and competing theories of how bank runs propagate. One theory (Diamond and Dybvig, 1983)

postulates that bank runs, driven by depositors who fear the actions of other investors, can occur

as an equilibrium phenomenon—even in banks that would remain fully solvent if no investors

withdrew. Once the (self-fulfilling) run starts, all bank customers seek the return of their deposits

as quickly as possible, since each customer’s withdrawal imposes a negative externality on all other

investors (due to the reduction in bank liquidity that results).

Another theory is that runs may be rationally driven by information: customers seek to discern

which banks are solvent and which are not (Jacklin and Bhattacharya, 1988). According to this

theory, only insolvent banks (or banks thought to be insolvent on the basis of public information)

face runs. These two theories have quite different implications for the potential for bank runs as

well as for optimal regulatory responses to this threat.

In the eyes of some investors, money market funds have become a substitute for bank deposits.

Retail investors often keep much of their liquid assets in money market funds to pay bills, purchase

consumer goods, or settle securities trades, and to transfer money back and forth to these funds as

liquidity is needed on (if necessary) a daily basis.1 Although investments in money market funds

are not guaranteed (as fund prospectuses and marketing literature clearly state), some investors

seem to believe that implicit guarantees exist, either from the management company or from the

U.S. Government. In addition, money funds have become a major short-term financing alternative

for corporations, who may be able to obtain cheaper financing by selling their short-term debt

to these funds, compared to bank loans or other financing methods. And, financial institutions,

including regulated banks, often invest in money market mutual funds to gain a higher yield with

their excess overnight liquidity.

In this paper, we study a run on money market mutual funds that developed during September

2008. Money market funds, as we explain, have certain characteristics in common with banks, but

1Some market observers have referred to money market mutual funds as “shadow banks,” although without anyexplicit guarantee of capital safety.

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also have significant differences. Owing to these similarities and differences, we believe that the

episode of September 2008 presents a unique opportunity to add perspective to the literature on

bank runs. This money market run is unique, relative to commercial bank runs, because data is

available on the types of investors who withdrew money each day, as well as the characteristics

of fund and management companies that suffered runs. Our main goal in this paper is to explore

the determinants of this episode, such as the institutional characteristics of different money market

funds, their portfolio holdings, and their investors, with a view to shedding light on the mechanisms

by which runs propagate.

During the days that followed the failure of Lehman Brothers on September 14, 2008, a single

money market fund that held Lehman debt securities, the Reserve Primary Fund, “broke the buck,”

that is, marked the net asset value of the fund below the $1 per share that investors normally

expect. This was the first time that a major money market fund had failed to return principal

of $1.00 per share to investors on demand.2 The event sparked vast outflows from the Reserve

Primary Fund. Within a few days, the panic spread to other money funds. Prime money market

funds—those that hold investment grade money market instruments, such as commercial paper,

repurchase agreements, bank certificates of deposit, and Eurodollar deposits—suffered outflows

totaling roughly $300 billion, even though many of these funds held little or no Lehman Brothers

debt. In contrast, government-only money market funds—those that invest primarily or exclusively

in Treasury and Agency securities—experienced vast inflows.

On September 19, 2008, the U.S. Treasury announced an optional program to “insure the

holdings of any publicly offered eligible money market mutual fund—both retail and institutional—

that pays a fee to participate in the program.” This insurance was available to money funds with

a shadow NAV of at least $0.995—ensuring that the NAV would be restored to $1—only for assets

invested prior to September 19, 2008. On the same day, the Federal Reserve instituted programs to

help provide liquidity to money market funds and the money markets more generally. Within a few

weeks, large “panic-driven” outflows from prime money market funds diminished and, ultimately,

2In 1994, a single small money market fund failed. However, that money market fund was closely held by agroup of banks. Consequently, its failure had little implication for other money market funds and their investors.The incidents of September 2008 were different: the Reserve Primary Fund was a very large fund, and its investorsincluded a broad range of retail and institutional customers.

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ceased.

We study the daily flows in money market mutual funds during this crisis period in an effort

to determine whether they were sparked by a widespread withdrawal of all investors (as modeled

by Diamond and Dybvig), or whether withdrawals were more information-based (as in Jacklin and

Bhattacharya). For instance, we wish to determine who initiated runs (institutional vs. individual

investors), as well as the timing of their withdrawals (e.g., whether they developed over several

days). Further, we wish to observe the reaction of the fund management companies, to determine

how they met the liquidations. Finally, we wish to determine the features of funds that were

susceptible to large-scale withdrawals, using fund size, complex characteristics, or portfolio weighted

average yield.

We first show that outflows of money market mutual funds during the crisis occurred in a very

heterogeneous way. Investors in a small minority of funds having certain characteristics exhibited

the bulk of run-like behavior, while investors in most other funds did not exhibit such behavior.

Next, we provide insights about the types of funds with run behavior. First, prime institutional

funds exhibited much larger persistence in outflows than retail funds, although retail investors also

exhibited some run-like behavior. Next, we find that funds that were larger, had a lower expense

ratio, were a member of a complex with a larger proportion of institutional money (in money market

funds), had higher lagged flow standard deviation, and had a higher portfolio-weighted promised

yield exhibited greater outflows during the crisis week of 2008.3 These findings are consistent with

run-like behavior predominantly occurring among more-sophisticated institutions (“hot money”)

that focused their trades on certain types of funds.

Specifically, hot money chased yields in larger, low-expense funds prior to the crisis, and con-

sidered the potential of a complex to “backstop” its institutional funds when deciding on whether

to move their money during the crisis. More exposed complexes exhibited a larger tendency to

experience investor run-like behavior.

We then implement both panel OLS models and quantile regression models to more formally

study the covariates of investor runs during the crisis. The quantile analysis accounts for changes

3A mutual fund “complex” is a family of mutual funds sponsored by a single provider who typically also managesthe group of funds. For example, some of the largest mutual fund complexes are those operated by Fidelity, Vanguard,Federated, and JP Morgan Chase.

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in the dynamics of fund flows during the crisis of September-October 2008 and for cross-sectional

heterogeneity in the effect of fund and investor characteristics on fund flows. Our focus on shifts

in the dynamics of fund flows is motivated by evidence indicating that fund flows behaved very

differently during and around the money market fund crisis triggered by the events leading up to

the failure of Lehman.

We focus on cross-sectional heterogeneity in fund flows for a few reasons. First, welfare and

policy implications may differ depending on whether outflows are evenly distributed across funds.

Imagine that there are 10 funds with identical observable characteristics and we observe a 5% net

outflow on a given day. If all funds have the same 5% outflow, each may be able to absorb the

shock with little impact–funds likely have plenty of liquid securities on hand to meet redemptions.

On the other hand, if 8 funds have no outflows and 2 funds face 25% redemptions, these funds are

likely to be in grave trouble. Large redemptions may force them to sell less liquid assets at fire

sale prices, potentially leading to the “liquidity spiral” discussed in Brunnermeier and Pedersen

(2009). Second, when examining the theoretical literature on bank runs and liquidity, a number

of models feature multiple equilibria. Even in those where equilibria are unique, small changes in

fundamentals can lead to large changes in outcomes. As such, we might have reason to expect

substantial heterogeneity in the fund flow distribution. Third, quantile regression is known to

have a variety of desirable robustness properties. OLS regressions can be much more sensitive to

leverage points and outliers in the data, relative to quantile regressions. Given that we are looking

at outflows during a crisis period with a number of large outflows, this property seems desirable.

Our results from estimations of these models indicate that withdrawals occurred unevenly across

funds and fund complexes during the September 2008 crisis. Funds that cater to institutional

investors, which are the most sophisticated and informed investors, were hardest hit. Certain fund

complexes were harder hit than others, suggesting that the events following September 15, 2008

were not purely panic driven. Instead, investors were, to some extent, discerning. Thus, during

September 2008, investor flows from money market funds seem to have been driven both by strategic

externalities (a la Diamond-Dybvig) and information (a la Jacklin and Bhattacharya).4 Our paper

4This seems consistent with steps taken by regulators and fund managers in an effort to stem the crisis. Forexample, on the regulatory side, the Treasury’s approach of guaranteeing money market funds makes sense to theextent that investor flows were driven by externalities: if investors simply panicked, a federal government guarantee

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provides some guidance to regulators and to fund complexes in forming strategies to avert another

such money market mutual fund crisis.

Our paper proceeds as follows. Section 2 provides a background on money market mutual

funds, and reviews the events of the crisis. In section 3, we provide a short literature review on

bank runs. Section 4 discusses our dataset, while section 5 discusses our methodology. Section

6 presents our main empirical results, while section 7 briefly presents some robustness tests. We

conclude in section 8.

2. Background

Banks and money market funds are similar in some respects (e.g., the presumption of dollar-

in-dollar-out) but quite different in others (e.g. vastly different regulatory structures, including

disclosure requirements). Thus, money funds provide a unique laboratory to study the mechanics

of runs. In addition, money fund data provide a unique opportunity, since we can observe daily

flows from investor types in each money market fund—institutional versus retail, as well as some

information on the particular type of institutional or retail investor (e.g. defined benefit pension

plans versus bank trust accounts). Further, we can observe characteristics of the management

company running the money fund. Such data is not available to researchers for banks and their

deposit accounts.

In principle, money market funds are much like other mutual funds. For instance, like other

mutual funds, they are regulated under the Investment Company Act of 1940 and its various

amendments (henceforth, ICA). However, they operate under a special provision of the ICA, Rule

2a-7, which lets them value investor shares at the “amortized cost” of assets—an accounting-based

rather than a market-based principle—that is, shares are valued at the purchase price of securities

minus computed premium or discount, amortized over the securities’ remaining life.5 This provision

of the ICA allows money funds to maintain essentially a constant $1.00 per share net asset value.

would likely dampen fears. The programs the Federal Reserve put in place to provide liquidity would also help,allowing money market funds that were otherwise stable to meet vast redemptions by nervous investors. On thefund manager side, many funds addressed information issues by publishing fund portfolio holdings more often andin greater detail than required by the SEC. Thus, in many cases, investors were able to tell on a daily basis whatsecurities their money market funds were holding.

5This is also how bonds are valued on accounting balance sheets of corporations.

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For investors, this has many advantages. It allows retail investors to use their money market funds

for transactions purposes, such as paying bills and settling securities trades. They are also able

to tie their money market funds to bank products, such as checking accounts, ATMs, and credit

cards. A constant $1.00 NAV allows many kinds of institutions (e.g. state and local governments)

to hold their liquid balances in money market funds, since they are restricted from investing in

variable NAV products. And, for both retail and institutional investors, a constant $1.00 NAV

vastly simplifies tax accounting by eliminating the need to track the capital gains and losses that

arise with a long-term mutual fund.

However, valuing money funds at amortized cost creates the potential for a run, because the

fund’s price per share (which is based on a book value) can diverge from the market value of the

fund’s underlying portfolio securities. Like banks, money market funds seek to offer highly liquid

liabilities, while holding less liquid assets. To be sure, this liquidity mismatch is much less extreme

for money market funds, but still raises the possibility that a money market fund might become

liquidity-constrained, unable to meet redemption requests, despite holding highly liquid assets.6

These risks have been controlled differently in banks and money funds. Banks are required to

maintain capital, and depositors are insured, but banks may generally hold highly illiquid assets

(e.g. 30-year mortgages), hold assets that may be lower-rated or difficult to rate or price, and may

employ leverage. Money market funds, in contrast, under Rule 2a-7, must hold only highly liquid,

high quality assets, and generally may not use leverage.7 The Securities and Exchange Commission

(SEC) and others have long recognized the potential exposure of money market funds; the SEC

has recently tightened the provisions under which money market funds operate, and are currently

considering further regulations to control this potential exposure.8

6This issue can also arise with long-term mutual funds. Mutual funds are required by law to offer investors theability to redeem their shares on a daily basis at the fund’s net asset value per share. It is at least theoreticallypossible that requests for redemptions could outstrip a fund’s ability to liquidate its underlying portfolio in orderto satisfy those redemptions. This possibility is more meaningful for bond mutual funds, such as during a financialcrisis if liquidity were to dry up in certain fixed income instruments (e.g., high yield bonds).

7Over the years, the provisions of Rule 2a-7 have been tightened to further reduce systemic risks (see Collins andMack).

8Specifically, amendments to Rule 2a-7, effective on May 5, 2010, have imposed several new requirements onmoney market mutual funds. These include, among other things: (??) requiring that a fund hold 10% of its portfoliovalue in securities that may be easily liquidated within one day, and 30% that may be easily liquidated within oneweek; (2) reduce the maximum weighted average maturity from 90 to 60 days, and (??) require fund managementto “know your clients” to judge flow volatility, and to increase asset liquidity, if necessary. Among other things, theSEC is considering further requiring funds to convert to a floating NAV.

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Prior to September 2008, Rule 2a-7 had worked well to control risks. From the point at which

Rule 2a-7 was first adopted by the SEC in 1983 to September 2008, a period during which hundreds

of banks and thrifts failed, only a single money market fund had “broken the buck” (i.e. failed to

return $1.00 per share). Even that event went largely unnoticed, because the fund was small and

held by a limited number of institutional investors (primarily banks).9 However, this changed on

September 16, 2008, when the Reserve Primary Fund “broke the buck.”10

Numerous traumatic economic events had occurred since August 2007, putting considerable

pressure on money market funds, as investors sought liquidity. From August 2007 to August 2008,

several unregulated liquidity pools used by institutional investors failed, both in the U.S. and

elsewhere. This led to vast inflows to money market funds, as these institutional investors turned

to the tighter regulatory provisions required of money market funds under Rule 2a-7.

Also, the federal government declined to assist a reeling Lehman Brothers, which failed on

September 15, 2008. On September 16, 2008, one large money market fund, the Reserve Primary

Fund, which held about $750 million in commercial paper issued by Lehman Brothers, disclosed

that it had broken the buck. Immediately, prime money market funds began to see vast outflows,

which totaled over $300 billion within a few days.11 With credit markets seizing up, prime money

market funds struggled to sell securities to meet these redemptions.

On Friday, September 19, 2008, the U.S. Treasury offered a guarantee to money funds in

exchange for an “insurance premium” payment. On that same day, the Federal Reserve announced

The Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility to provide

funding to U.S. depository institutions and bank holding companies to finance purchases of high-

quality asset-backed commercial paper (ABCP) from money market mutual funds under certain

conditions. This program was set up to assist money funds holding such paper to meet redemption

demands and to promote liquidity in the ABCP market and money markets more generally. This

program began operations on September 22, 2008, and was closed on February 1, 2010.

9The Community Bankers US Government Fund broke the buck in 1994. It was an institutional fund, and paidinvestors 96 cents per share.

10It should be noted that there are several instances where the $1 NAV would have been broken, had the fundadvisor not intervened by injecting capital or buying poorly performing assets.

11There is some debate about this figure, as State Street apparently froze the assets of the Reserve Primary Fundafter the exodus of about $10 billion. The $300 billion figure includes redemptions that were sent, but not honoredby shareholders of this fund. Source: Pete Crane of Crane Data, LLC.

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In addition, in response to the growing difficulty of corporations in rolling over their short-term

commercial paper, the Fed announced The Commercial Paper Funding Facility on October 7, 2008,

followed by additional details on October 14, 2008. This program took effect on October 27, 2008,

and was designed to provide credit to a special purpose vehicle that would purchase three-month

commercial paper from U.S. issuers.

On October 21, 2008, the Federal Reserve announced another program, The Money Market

Investor Funding Facility October 21, 2008, the Federal Reserve announced another program, The

Money Market Investor Funding Facility (MMIFF). The MMIFF was a credit facility provided by

the Federal Reserve to a series of special purpose vehicles established by the private sector. Each

SPV was able purchase eligible money market instruments from eligible investors using financing

from the MMIFF and from the issuance of asset-backed commercial paper (ABCP). Eligible Assets

included certificates of deposit, bank notes and commercial paper with a remaining maturity of at

least seven days and no more than 90 days.

In addition, the SEC took a number of actions, perhaps the most important of which was to

allow money market funds to price their underlying securities at amortized cost at a point during

the crisis when quotes on commercial paper were generally regarded as unreliable.12 Following

these developments, investors continued to redeem shares from prime money market funds, but at

a diminishing rate. By the end of October 2008, redemptions essentially ceased.

To illustrate, Figure 1 (top panel) shows daily changes in the net assets of the Reserve Primary

fund during September 2008. Severe outflows began on September 13, 2008—perhaps due to some

sophisticated investors anticipating that the fund’s holdings of Lehman securities were in peril. By

the end of September 2008, the Reserve Primary Fund had stemmed outflows. The bottom panel

of Figure 1 shows flows for all six aggregate fund categories of funds over the same month. Clearly,

prime institutional funds were most severely affected, with prime retail and tax-free funds also

showing significant daily outflows.

In contrast, money market funds holding U.S. Government-backed securities (mainly Treasuries

and agencies) experienced strong inflows, as investors sought the liquidity of the Treasury market

12This was an interesting development, especially since the SEC later proposed a floating NAV as a potentialsolution to runs on money market funds.

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as safety. Figure 2 shows, for government and prime money funds, the daily flows in billions of

dollars. Note that the magnitude of the outflows in prime institutional funds is magnified, due to

the large scale of these investments, relative to the outflows in prime retail funds.

3. The Literature on Runs

Several areas of the literature on bank runs are relevant for our study of runs on money market

mutual funds. An early literature on bank runs, most notably Diamond and Dybvig (DD; 1983),

model how a bank run can be an equilibrium outcome with bank deposits, in the absence of

asymmetric information between banks and investors. In DD’s equilibrium, investors think the

bank is going to fail, and it becomes a self-fulfilling prophecy, that is, the bank would not fail if

investors do not run.

Instead of attributing bank runs to such indiscriminate “animal spirits,” Jacklin and Bhat-

tacharya (1988) model information-based bank runs, which are driven by two-sided asymmetric

information—the bank cannot observe the liquidity needs of the depositors, and depositors cannot

observe the bank asset quality. The intermediate arrival of information about the quality of a

bank’s portfolio can trigger a run.

More recently, some papers use global games with regime shifts to study runs on banks. Angele-

tos, Hellwig, and Pavan (2007) analyze a setting in which regime changes occur as a result of the

decision of a sufficiently large proportion of agents to attack the current (status quo) regime. They

show that multiple equilibria can arise even after allowing agents to learn about the underlying

fundamentals. While fundamentals have predictive information about the stability of the current

equilibrium state, they generally do not determine the exact time of the breakdown.

Interestingly, conditional on the underlying fundamentals, an equilibrium can entail oscillations

between periods of calm, where no traders attack the current state (e.g., exchange rate), versus

periods of market distress, in which a regime change is a likely outcome.

Angeletos et al. (2007) show that the stability and multiplicity of equilibria are linked to the

arrival of new private information. In the absence of any new information, an equilibrium exists

in which an unsuccessful attack in the first period is followed by no regime shift in all subsequent

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periods. Conversely, the arrival of new private information makes future regime changes possible.

Moreover, the equilibrium will, at any point in time, depend both on the amount of private and

public information available, as well as on the history of how the equilibrium responded to attacks

in the past. That is, after a failed attack, only a significant increase in the precision of private

information signals will make future regime changes possible.13

These theories are relevant to our analysis of the money market mutual fund crisis, since money

market funds escaped largely unscathed from the failure of Bear Stearns in April 2008. This

suggests that a large shock to agents’ information was required to explain the massive outflows

from money market funds during September 2008 following the collapse of Lehmann.

In our setting, it is likely that some investors, e.g., hedge funds, are more informed than others,

e.g., corporate retirement funds, about the liquidity of portfolio holdings and the tendency of other

investors to run. It is broadly believed that, prior to the crisis of September 2008, there were

investors with “smart money,” who closely monitored the yields and stability of money market

funds, while there were other investors with “dumb money,” who did not.14

In a related study, Jank and Wedow (2010) study German money funds during the crisis. They

find that German money funds competed for flows, which respond to past performance of funds,

by investing in less liquid securities prior to the financial crisis. Funds that engaged in this risk-

shifting to the greatest degree suffered larger outflows during the crisis, i.e., investors appeared to

understand the potential for runs in funds with less liquid holdings. Therefore, less liquid funds

attract flows at the risk of developing an investor run if a market distortion occurs (such as the

Lehman default).15

In another related study, Chen, Goldstein and Jiang (2010) document the presence of strategic

13Some models proposed in the literature also allow small shifts in fundamentals or in traders’ information to havedisproportionately large effects (nonlinearities) on equilibrium outcomes (e.g., Morris and Shin, 1998). This may notbe a necessary consideration here, however, since the Lehmann bankruptcy by no measure was a ”small” shock.

14“Dumb money” can occur due to agency costs. For example, a hedge fund manager is compensated directly fromfund profits, while a treasurer who oversees a corporate retirement plan may face much more limited compensationincentives. The hedge fund manager, therefore, is more likely to expend search costs to frequently gather informationabout each money market fund.

15It is interesting, also, in light of the recent call of many from the Securities and Exchange Commission for aconversion of U.S. money market mutual funds to a floating NAV standard, that the German money funds in theJank and Wedow sample had floating NAVs. However, there is some debate about the definition of “money marketfunds” in Europe, since ultrashort bond funds are sometimes categorized as money market funds in Europe. Thefunds in the Jank and Wedow sample likely included what U.S. investors would consider ultrashort bond funds.

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complementarities investors in non-money market mutual funds. They explore whether the sensi-

tivity of outflows to bad past performance is greater among illiquid funds, for which the strategic

complementarities are greater, compared with more liquid funds, for which such effects are much

smaller because redemption costs are much lower. They also explore whether funds dominated

by large investors are less subject to runs induced by strategic complementarities because large

investors will internalize the externalities induced by their withdrawals.

4. Data and Initial Results

We first describe our data and present some initial empirical results based on non-parametric

analysis.

4.1. Data

Our data are derived from iMoneyNet (www.imoneynet.com). iMoneynet collects daily information

for over 2,000 U.S. registered money market mutual funds that invest primarily in U.S. short-term,

dollar-denominated debt obligations. These money funds are offered to retail as well as institutional

investors. The iMoneyNet data consists of daily total net assets by share class, as well as data on

the fund investment objective, fund family/adviser (i.e., “complex”), fund type (i.e. retail vs.

institutional), portfolio average maturity, and asset breakdown. Our daily money fund dataset

from iMoneyNet covers the period December 31, 2007 to June 30, 2009. We approximate daily

fund share class flows as the daily change in share class assets (this ignores the negligible effect of

the small daily dividends that money market funds declare).16

Table 1 shows the total net assets and number of shareholder accounts in these funds at the

end of each year from 1990 to 2008. The total assets held in money market funds increases from

almost $500 billion at the end of 1990 to more than $3.8 trillion at the end of 2008, reflecting the

huge increase in popularity of these funds among individuals and institutions. As of December

2008, the total M2 money supply in the United States was $8.3 trillion, of which $1.3 trillion was

16Almost all money fund dividends are reinvested in the same money fund share class, so distributions (and theirpassive reinvestments) have a negligible effect on our estimates of flows.

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invested in retail share classes of money market mutual funds.17 Therefore, money market funds

are a substantial share of liquid assets for individuals in the U.S. Note that, until the crisis of 2008,

money market funds holding predominantly non-government securities dominated the asset value

of the sample. During 2008, assets held in government money market funds nearly doubled to $1.45

trillion. Note, also, that there were 38 million shareholder accounts as of December 2008, most of

which were retail accounts. Therefore, a large cross-section of individuals in the U.S. hold assets in

money market funds, indicating that any perceived instability of money funds may impact a large

proportion of U.S. investors. In terms of assets, institutions own about two-thirds of money funds.

Therefore, even outflows from a relatively small number of institutional accounts could impact

money funds.

Table 2 explores the yearly number of money market mutual funds and the yearly number of

shareclasses within each asset-class category. Note that the average number of shareclasses per

fund has increased from slightly over one to almost three during the sample period. This trend

indicates that the actions of one class of investors has a greater potential to affect other classes

over time, such as institutions affecting individuals, and vice-versa.

Table 3 shows the net cash inflows, as well as a breakdown into sales and redemptions for each

year from 1984 to 2008. The table shows that flows into money market funds have been vastly

increasing over the sample period, especially since about 1995. Note, also, the movement toward

money funds during the market crisis of 2007 and 2008.

It is important to understand the characteristics of portfolio holdings of money funds to ex-

plore the liquidity and other risks that they carry. Table 4 shows that funds that focus on non-

governmental securities made a secular movement from commercial paper to bank CDs, repurchase

agreements, and corporate notes over our sample period. Clearly, funds have become more diver-

sified among asset classes, while retaining a weighted-average asset maturity of almost 50 days.

17M2 consists of currency, traveler’s checks, demand deposits, other checkable deposits, retail money market mutualfunds, savings, and small time deposits. See www.federalreserve.gov/releases/h6/hist/h6hist1.txt for yearly values ofM2 and www.ici.org for yearly retail money fund assets.

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4.2. Data Analysis: Non-parametric Results

To motivate our model-based analysis, we present summary graphical evidence of fund flow pat-

terns around the money market fund crisis, and the extent of cross-sectional heterogeneity in such

patterns during and around the time of the crisis.

4.2.1. Heterogeneity in Outflows Across Funds

Figure 2 shows sizeable aggregate outflows from our sample of prime money market funds during

the crisis. However, it does not show to what extent all funds experienced similar outflows or

whether, alternatively, a few outlier funds experienced disproportionate outflows. The degree of

heterogeneity in fund outflows is important to address for several reasons. First, individual funds

differ both in terms of the riskiness of their holdings and in terms of their investor characteristics,

so understanding the distribution of these characteristics, and their impact on fund flows, is crucial

prior to specifying our empirical models. Second, from a policy perspective, we are interested in

exploring whether regulatory actions should be focused on the entire industry or on just a small

segment of vulnerable funds.

Third, in markets with frictions, optimal policies are likely to be sensitive to the ex-post dis-

tribution of outflows across funds. This is true even if all funds face the same ex-ante run risk,

particularly if there is non-trivial heterogeneity in portfolio holdings. This would be the case if,

for example, fund-level redemption costs are a convex function of outflows. If outflows are rela-

tively equally spread across funds, then downward price pressure is likely to be relatively equally

distributed across assets with similar risk characteristics, making it relatively easy to match buyers

with sellers without large price declines. If runs are concentrated at a small number of funds, those

assets that were overweighted by these funds are likely to face substantial selling pressure, poten-

tially leading to fire sales. Such fire sales could cause funds’ shadow NAV to drop further below $1,

further increasing investors’ incentives to run on hard-hit funds in following periods.18 Not only

might redemptions be absorbed more easily in the former relative to the latter case, but there is

also less likelihood of amplification in later periods (particularly if some “hot money” leaves the

18These fire sales could also create lucrative opportunities for funds which were not hard hit, since yields on thedistressed assets would rise due to these frictions, creating an additional incentive for investors to move their money.

13

market in the initial wave of redemptions).

Finally, given our interest in testing whether the data are consistent with classes of models

with multiple or fragile equilibria, average flows may not be a sufficient statistic for runs. Even in

classes of models with unique equilibria, investors may have private information and/or condition on

state variables which are unobservable to the econometrician. For example, imagine that investors

only have the choice between investing in two funds, A and B, which have the same observable

characteristics (including, but not limited to, fund size). If investors redeem all of their shares from

fund A and invest the proceeds in fund B (i.e. fund A gets hit with the run equilibrium while fund

B does not), the average flow does not change despite the fact that one fund was wiped out and

the other fund doubled in size. However, we get a more complete picture if we look at the tails of

the flow distribution. Naturally, this is an extreme example since investors have additional asset

classes at their disposal, but it does suggest that some interesting dynamics could be missed by

restricting attention to average outcomes.

These arguments, among others, suggest that valuable insights into the nature of the fund flows

during the crisis can be gained by examining the distribution of flows on each day. For concreteness,

we focus our attention on the percentage outflows in the 10th, 50th (median), and 90th quantiles

(ranked by outflows) of funds, though the pictures are similar for other quantiles as well. Figure 3

shows such quantile estimates for prime institutional funds (top panel) and for prime retail funds

(bottom panel).

Turning to the top panel of Figure 3, note that the distribution of percentage flows is fairly

tight, as indicated by the vertical distance between the 10th and 90th quantiles, during the period

ending one week prior to the Lehman bankruptcy announcement (i.e., up to Friday, September

5, 2008). Then, the distribution widens, as shown by the days prior to the Lehman bankruptcy

(i.e., from September 8-12). Finally, the distribution further substantially widens on September

15, and the following couple of days, before again tightening and becoming more stable. This

finding indicates that investors viewed funds as being very heterogeneous. While the median prime

institutional fund experienced only mild outflows during the crisis, funds in the left tail (of the

outflow distribution) experienced extremely large outflows. For instance, the outflow on September

14

17 approached 20% of total net assets in the 10th quantile – in other words, the 10% worst affected

funds experienced outflows of 20% or higher of prior-day total net assets on a single day! However,

on that same day, the median fund quantile experienced average outflows of only about 2%. Finally,

while the distance between 10th and 90th quantiles decreases substantially, it remain wider than its

value during early September for several weeks following the week of September 15-19.

The lower panel of Figure 3 shows results for prime retail funds. While the pattern of the

distribution is qualitatively similar to that of prime institutional funds, the distribution remains

much tighter (note that the graph has been magnified, so that the difference between the 10th and

90th quantiles are, at most, roughly 5% of total net assets on September 18.) This result indicates,

as we have shown earlier in this paper, that retail investors did not respond to the crisis in a

significant way, although there is evidence of a “mild-run” in the left tail of the distribution.

In conclusion, Figure 3 shows considerable heterogeneity in the behavior of prime institutional

fund (net) flows around the time of the crisis. Whereas the median fund hardly experienced any

outflows, even at the peak of the crisis on September 17, the bottom 10% of funds saw outflows in

excess of 20% on a single day, followed by outflows around 10% on the day after and more than

5% on the third day of the crisis. In sharp contrast, more than 10% of the funds experienced

inflows on all days during the crisis, which may have originated from funds experiencing outflows.

A similar, but more muted pattern is seen in prime retail fund flows. For these funds, the median

fund experienced very small net flows during the crisis, whereas the top 10% of funds experienced

steady inflows of about one percent per day on most days.

4.2.2. Persistence in Flows

So far we have documented the size of total outflows, as well as cross-sectional differences in outflows.

In this section, we study flow dynamics, which can help to shed light on possible liquidity spirals,

see, e.g., Brunnermeier and Pedersen (2008). Their analysis suggests that large outflows (which

have a very similar effect to margin calls) in isolation may not be very costly when markets are

liquid, since fire sales are less likely to occur. In our application, illiquid markets can dramatically

amplify strategic complementarities associated with constant NAVs, thereby increasing run risk

15

for all funds. However, for funds which recently faced large redemptions, we might expect the

amplification mechanism to be even greater. If funds must sell their most liquid assets to meet

initial redemptions, any additional redemptions are more likely to be met by selling illiquid assets.

In a more information-based context, recent outflows could cause remaining investors to update

their beliefs about the quality of the fund’s portfolio and/or other investors’ preferences, which

could trigger additional outflows in the subsequent period, particularly if market liquidity dries

up.If persistence in outflows is especially high among funds with the highest prior-day outflows,

then this suggests that prior withdrawals can create an amplification effect and potentially lead to

runs on individual funds in the absence of a mechanism for stopping the run.

Thus far, we have focused on amplification mechanisms and said very little about dampening

mechanisms, i.e. how runs might eventually end, short of funds closing down. Before examining

the data, it may be helpful to discuss a few such mechanisms. First, if there are multiple types

of investors (e.g. patient and impatient, uninformed and informed) within funds with different

levels of liquidity needs, outflows may cause the majority of the more run-prone investors to leave

the fund. Since there is no means by which these riskier types can take short positions in funds,

the change in investor composition that occurs once they redeem their shares can reduce the role

of asymmetry of information about depositors in future periods.Second, if markets return to a

liquid state, either via regulatory actions or changes in other aggregate state variables, we would

expect persistence to decline. The Federal Reserve AMLF program had precisely this goal in mind,

reducing the costs associated with selling asset-backed commercial paper, when it was implemented

following two days of massive redemptions from funds. Third, investors’ observations that a given

fund is able to absorb large redemptions without breaking the buck can send a positive signal about

funds’ asset quality (or sponsors’ willingness to intervene), reducing the run risk in future periods.

To summarize whether the average persistence of fund flows changed around the time of the

crisis, we first provide estimates based on panel regressions of the form

yit = αt + φyit−1 + εit, (1)

where yit = (TNAit − TNAit−1)/TNAit−1 is the estimated flow rate for fund i on day t. The

16

persistence measure, φ, is obtained using a five-day rolling estimation window. This specification

is one of the simplest possible which allows us to decompose flows into aggregate and fund-specific

sources of persistence in outflows. For example, since category-level outflows were positively auto-

correlated throughout the crisis, we would expect to see autocorrelation in individual fund flows

even if no amplification mechanisms were present. The addition of αt soaks up these sources of vari-

ation (e.g. category-level flows, macroeconomic variables, market returns, etc.), so the coefficient

φ captures persistence which is orthogonal to a common aggregate shock. In many of the results

in the following section, we will employ time dummies in a similar manner in order to control for

common shocks before examining the role of fund-specific variables.

Figure 4 presents the AR(1) coefficient from this regression for prime institutional funds (top

row of graphs) and prime retail funds (bottom row). The horizontal axis corresponds to the last day

of the 5-day rolling window, so that, for example, the point estimate corresponding with September

19th on the graph would be estimated using the first week of flows following the Lehman bankruptcy.

Prior to the crisis, the average daily autocorrelation in prime institutional flows hovers near zero.

During the crisis, the autocorrelation spikes to almost 0.6, followed by another spike that exceeds

0.4 at the end of September, before subsuming again. This illustrates that fund flow persistence

increases dramatically during and after the crisis, consistent with run behavior by investors.

Note that while these coefficients are substantial, the absolute value of φ is significantly less

than 1 in all periods, suggesting that fund-specific amplification effects would eventually die out

over time. By itself, this observation would be consistent with the mechanisms that end runs at

the fund-level eventually kicking in. However, there are at least two reasons why this might not

be the case. First, this simple exercise ignores the survivorship bias introduced by a few funds

dropping out of our sample, which would be likely to bias our estimates of φ towards zero. Second,

we cannot observe the counterfactual path of φ in the absence of the two unconventional actions

taken by the Federal Reserve during the crisis. Unfortunately, the data are silent about whether

these amplification measures would have continued to creep up (potentially leading to a complete

market failure) or would have died out on their own without any regulatory interventions.

The plots for the prime retail funds show that the average autocorrelation in these funds’ flows

17

went from negative values prior to the crisis to a slightly positive value, only to decline to the

negative range before experiencing a local peak again (albeit at a negative value) in early October.

It is important to note that negative autocorrelation can occur for many reasons unrelated to any

type of strategic behavior by investors. For example, it is not unusual for investors to sell stocks

or bonds, which results in money being credited to a money fund, then to transfer the money to

a checking account or to purchase other stocks or bonds. Stock purchases can operate in a similar

manner. However, since settlement occurs with some delay, the money fund account experiences

negatively correlated flows due to these normal portfolio rebalancing activities.

To gain a different perspective to the persistence in outflows, we sort funds into three equally-

sized groups based on the previous day’s fraction flows, then look at subsequent flows for funds

within each group. An advantage of sorting funds in this manner is that we impose very little

functional form on the nature of persistence since we are only conditioning on funds’ relative

rankings. Since these relative orderings are invariant to common shocks (e.g. changes in cross-

sectional means and variances of the flow distribution), the nature of the conditioning information

may be more stable over time. If past outflows are an important predictor of future outflows, we

would expect the fortunes of each group to differ substantially. If conditioning on past flows adds

little additional information, then we would expect the groups to look fairly similar. Therefore, if

persistence changed during the crisis, we would expect the differences between groups to change

accordingly.

Once we sort funds into terciles, we need to choose an outcome measure with which to compare

them. For this analysis, we focus on cumulative flows over the next 5 trading days. We choose a

weekly horizon in order to integrate out some of the amplification or dampening mechanisms present

in the daily flow distributions in order to learn something about total redemptions. However, we

limit the total window size in order to get a sense of the quantity of assets which funds were forced

to liquidate over relatively short time horizons. These tradeoffs are similar to those associated with

value-at-risk calculations. One wants to look at multi-period payoff distributions to capture total

portfolio losses. However, the time horizon should not be so long that a firm could easily close out

its position if it observed negative realizations in the first period. We repeat the analysis using

18

daily flow distributions, and the results are qualitatively similar, though the magnitudes of the

outflows are smaller.

Figure 5 plots the 10th, 50th, and 90th quantiles of subsequent weekly flows for within each

lagged flow tercile. The horizontal axis corresponds with the date of the flows according to which

funds are sorted. Looking at September 16th on the graph, funds would be separated into terciles

based upon flows on that date, and the plotted quantiles correspond with cumulative flows from

September 17-23. In addition to these quantiles, we add two vertical lines corresponding with

September 10 and September 16. These dates are the first and last dates, respectively, for which

the subsequent weekly period includes September 17th, the day with the largest category-level

outflows.

Let us first turn our attention to the left panel, which focuses on prime institutional funds. The

three groups look relatively similar prior to September 10, except that that the bands are slightly

wider for terciles 1 and 3, reflecting the fact that funds with higher unconditional variances tend

to end up in these terciles. Also, daily flows appear to exhibit ARCH(1)-type effects, likely due

to portfolio rebalancing-type activities. For all three lagged flow terciles, we observe that the 10th

percentile drops considerably after September 10. This occurs because the 1 week window begins

to include more data from the crisis period. If we compare the quantiles on 9/10, we observe

that the differences between the terciles are relatively minor. All three distributions are highly

asymmetric, with a median relatively close to zero and a 90th percentiles slightly above zero, but

a 10th percentile of -25%. This suggests that lagged flows provided very little information about

future flows at the onset of the crisis, and the asymmetry reflects the substantial cross-sectional

heterogeneity documented in Figure 3.

As we move from September 10 to September 16, more substantial differences begin to emerge.

On September 11, terciles 1 and 3 continue to look fairly similar, having a lower median and a lower

10th percentile. The quantiles for tercile 2 remain essentially unchanged. We find that funds with

volatilie pre-crisis flows continue to have more volatile flows during the crisis, so funds sorted into

terciles 1 and 3 are likely to have higher unconditional volatilities relative to tercile 2. As we move

closer to September 16, we observe that the median and 10th percentiles continue to trend sharply

19

downwards for tercile 1, while they begin to trend upward for tercile 3. For tercile two, the median

drops to about -5% and then levels off, while the 10th percentile trends upward. These results

suggest that lagged flows begin to provide substantially more predictive information about future

flows during the days prior to September 16. To see this, note that the median fund in tercile

1 (those with the largest outflows on September 16), lost more than 20% during the subsequent

week, while only 10% of funds in terciles 1 and 3 had outflows of this magnitude or larger. The

10% hardest hit funds in tercile 1 on September 16 faced almost 45% in redemptions over the next

week! In tercile 3, the median fund had essentially no redemptions, while the top decile experienced

sizable inflows during the crisis period–the 90th percentile peaks out at almost 20% on September

12 and stays north of 5% –suggesting that some of these funds may have been regarded as safe

havens.

Outflows remain highly persistent during September 17-19, the period with massive category-

level redemptions. Therefore, in addition to having to liquidate assets to cover large redemptions

today, a fund in the bottom tercile was likely to face substantial redemptions over the next week.

For example, every fund in tercile 1 faced at least 5% in redemptions on September 17 and 18

(most funds faced much more), so the quantiles plotted in Figure 5 are redemptions in addition

to that 5%. Throughout this period, the median flow for tercile 1 is approximately equal to the

10th percentile flow for terciles 2 and 3, and the 10th percentile outflow for tercile 1 is extremely

large. One thing that does change after September 16, however, is that the right tail for tercile 1

does start to expand relative to the other groups. As such, some capital did return to some of the

hardest hit funds during the following week, but only for a subset of funds.

During the last week of September and the early part of October, things start to stabilize. The

median outflow for tercile 1 is generally a bit lower relative to tercile 2, but its left and right tails

look fairly similar to those in the other groups. As before, terciles 1 and 3 look fairly similar,

reflecting the fact that large flows today are likely to be correlated with large flows tomorrow.

Unlike the crisis period, however, the direction of subsequent flows becomes harder to predict.

This pattern is similar to the one we observe in data prior to the crisis, namely that flows are

heteroskedastic (exhibiting ARCH(1)-type effects) but not very highly autocorrelated.

20

The picture for retail flows is a bit noisier, and the magnitudes are much smaller. As with

institutional flows, we find that terciles 1 and 3 look relatively similar, reflecting conditional het-

eroskedasticity in retail flows. The median flow in tercile 1 does drift downward for tercile 1 from

September 10-16, but a similar pattern emerges for tercile 3. After September 16, the main differ-

ence between the two is that the entire distribution for tercile 1 appears to be shifted downward

relative to tercile 3 by about 2.5 percentage points. Median flows for each category are mostly

monotonic, in that the median fund in tercile 1 was worse off than the median fund in tercile 2,

which was worse off than the median fund in tercile 3. However, overall persistence in retail flows

is much more muted relative to institutional flows. We also note that some retail funds in terciles

2 and 3 began to receive sizable inflows in the beginning of October.

An alternative nonparametric measure of persistence in fund flows is provided by a transition

matrix that shows how individual funds transition across groups sorted by prior-day fraction flows.

These pictures answer the following question: given that a fund is in tercile i on day t, what is a

probability that it is in tercile j of the distribution of cumulative flows from days t + 1 to t + 5?

For the terciles described above, we compute the aggregate flows for each fund within each tercile

over the following five trading days. Finally, we rank funds into new terciles based on this five-

day percentage flow, then compute the proportion of funds, from each first-day tercile, that end

up in each following five-day tercile. This process is repeated each day, and the daily transition

probabilities are plotted in Figure 6.

The two panels on the left side of the figure show results for top and bottom terciles, respectively,

of prime institutional funds. For instance, the top plot on the left side shows that about 35% of

funds in the bottom flow tercile, T1 (those with the biggest outflows), on September 1 end up in the

bottom flow tercile over the following five days, close to the 33% that we would expect if there were

no persistence in outflows. These numbers are slightly higher than 33% given the heteroskedasticity

addressed above. Since some funds have more volatile flows than others, they are more likely to

oscillate between terciles 1 and 3 than they are to end up in tercile 2. This Tercile 1 graph shows

that transitions occur roughly randomly (close to 33%) up to about one week prior to September

17. From September 10-16, the probability of a fund remaining in tercile 1 begins to increase,

21

as the flow persistence increases. This persistence measure peaks upwards of 60% on Septemper

17-19, precisely the period where redemptions were extremely large. Eventually, then the transition

probabilities subside, though the probability that a bottom fund moves to the top tercile remains

somewhat lower. Note that pattern in the top tercile (the bottom graph) is consistent with Tercile

1 driving most of the transitions. That is, during the crisis, funds in Terciles 2 and 3 tend to remain

in one of these two terciles, rather than moving to Tercile 1.

The time-series patterns in transition probabilities indicate that the majority of persistence in

flows during the peak weeks of the crisis occur among the highest prior-day outflow funds.19 High

outflow funds during one day tend to remain high outflow funds during the following days. This

evidence is, again, consistent with our prior findings of this paper—that investor runs during the

money fund crisis were highly concentrated among a small segment of funds.

Similar transition probability plots are shown on the right side of the figure for prime retail

funds. Interestingly, although persistence in outflows does not appear to be as dramatic as it is

for prime institutional funds during the week following the Lehman bankruptcy, the persistence

of funds in Tercile 1 remains higher than expected (under random transitions) for a much longer

period. This finding indicates that retail investors continued to move their money out of funds

that had experienced initial outflows, even after the Treasury and Federal Reserve had announced

insurance and support programs. Perhaps retail fund investors exhibited a delayed reaction to the

money fund crisis, as well as to the rescue programs of the Treasury and Fed. However, some of

this persistence is driven by predictability in retail inflows rather than outflows, as can be seen by

examining the 90th percentiles of Figure 5. If retail investors are less likely to distinguish between

government and prime money categories relative to institutional investors, then proceeds from

stock sales might be more likely to move into prime funds. If some money funds were more likely

to be attached to brokerage accounts than others, persistence in inflows could simply reflect retail

investors’ movements away from equities towards cash positions.

To summarize, Figure 6 shows that prime institutional funds that experience large initial out-

flows during the crisis continue to see massive outflows over the next five days. Similarly, funds in

19We also try conditioning on lagged flows over longer periods, but we find that persistence is strongest when usingprior day flows only.

22

the top (third) tercile, i.e., funds that experience large initial inflows (or smaller outflows) during

the crisis continue to see further inflows during the following days. The findings suggest consid-

erable persistence in the funds’ flows during the period: those funds that initially experienced a

large outflow were highly likely to see a continuation of being among the funds with the greatest

subsequent outflows during the ensuing five-day period. This also suggests separation of funds into

“types” and only scant evidence of mean reversion in (relative) fund flows during and after the

crisis period.

The persistence in outflows during the crisis could be due to some funds having “fundamentals”

that were viewed by investors as being more risky than other funds. Or, it could also be due to

investors exhibiting an amplified aversion to funds with certain fundamentals. Finally, it can also

be due to investors basing their redemption decisions purely based on prior-day flow information,

independent of fundamentals. Our next sections explore which of these alternative explanations

appear to drive the extreme outflows experienced by some funds during the crisis.

4.2.3. Effect of Covariates

We next determine whether funds with different characteristics experience different outflows during

the crisis. To accomplish this, we study the effect of five characteristics, measured as of September

10, 2008:

• Average yield, AV GY IELD, defined as the average value of the (annualized) 7-day gross

yield from March-August 2008,

• Fund-level expense ratio, EXPR, calculated as a TNA-weighted average of shareclass-level

expense ratios,

• Standard deviation of daily logarithmic fund flows, FLOWSTDEV , calculated using data

from March-August 2008,

• Proportion of money market mutual fund assets represented by institutional shareclasses at

the complex level, PIPERC, and

23

• Fund size, LOGTNA (the sum of total net assets of all shareclasses of a given money market

fund).

Figure 7 presents univariate plots of the daily quantiles for prime institutional fund flows for the

bottom (the five left panels) and top (the five right panels) terciles formed on fund characteristics.

The plots show that funds with the largest yields (top tercile, shown in the graph on the top right)

exhibit the greatest outflows, with outflows being particularly strong among the bottom decile of

these high yield money funds. Funds with higher yields are likely to hold riskier portfolios that

are harder to liquidate, and, so, could be more exposed to runs. This pattern is strong evidence

that prime institutional investors understood the risks of different funds, and focused their “run

behavior” on those funds most exposed.

We see a much bigger effect of expense ratio on fund flows among funds in the lowest tercile

of the expense ratio distribution, implying that funds with the highest expense ratio saw much

smaller outflows than funds with small expense ratios (which are predominantly more-sophisticated

institutional investors). Funds in the top third of flow volatility are more likely to experience

either very large outflows or large inflows, compared with funds in the bottom tercile. This pattern

indicates a that certain funds are exposed to a “hot money” effect; that is, sophisticated institutions

appear to focus their investments on certain funds. A large percentage of institutional money,

measured at the fund complex level, also seems to increase the magnitude of outflows, indicating

that investors considered whether the complex could withstand a run by its institutional investors

(i.e., whether the complex could credibly backstop the funds). This variable can also be interpreted

as a proxy for sponsor reputation concerns, a channel identified in Kacperczyk and Schnabl (2012).20

Finally, there is a very clear effect of fund size, with the largest funds clearly being more strongly

exposed to outflows than the smallest funds.

Another aspect of these plots deserves some discussion, namely the heterogeneous relation

between covariates and subsequent outflows. We generally observe that the 90th percentiles remain

20Runs in the riskiest prime institutional category could reflect poorly on complexes’ reputations, leading to with-drawals in other categories. If these potential losses of management fees from other categories exceed the directcost of supporting the prime institutional funds, one would expect the sponsors to intervene in order to protect theprofitability of their business as a whole. As such, complexes with a relatively low fraction of assets in the (mostrun-prone) prime institutional category may be more willing to support struggling funds.

24

quite similar as we move from the left panel to the right panel. Once again, some funds were

barely hit at all or received inflows, and no single covariate is capable of identifying a subset of

funds which were guaranteed to receive large outflows using this method. The differences are more

pronounced for median outflows, and become extremely stark when comparing the fortunes of

the hardest hit funds in each group. Generally, if a variable tends to cause redemptions for the

median fund to become larger, then the spread between redemptions for the median and the 10th

percentile funds within each group also tends to increase, consistent with nonlinear dependence

between outflows and predictor variables.21 This nonlinear dependence is particularly pronounced

prior to September 17. For example, the median and 10th percentiles of flows for the top and

bottom expense ratio terciles are quite similar prior to September 16, but the spread between the

median and 10th percentile starts to expand for low expense ratio funds throughout this period.

As will continue to be the case when we present our results below, there often is a stronger

relationship between covariates and the 10th percentile of the flow distribution relative to the other

percentiles. In other words, it is easier to establish a relationship between conditioning variables

and flows if we focus on the funds which are hardest hit. Median flows also tend to respond,

but this tends to occur only once the full-scale run begins on September 17 and the magnitudes

tend to be smaller. This indicates the presence of additional variables which determine the size of

runs. In an information-based context, investors may have additional private or public information

about portfolio holdings or liquidity needs of other investors. Furthermore, given the likelihood of

encountering multiple equilibria, investors could be responding to different sunspot-type variables

in deciding whether to redeem shares on a given day.

Figure 8 shows a set of similar non-parametric plots for prime retail funds. The results are

somewhat similar, though a lot noisier relative to institutional flows. Again, we see that funds

at the bottom tercile of the expense ratio distribution experience larger outflows than funds at

the higher end of the expense ratio distribution. Similarly, funds with the highest prior-measured

volatility of flows have greater outflows and inflows than funds with lower flow volatility. Finally,

21Such a phenomenon could be consistent of nonlinear dependence, but it could also occur even, e.g. there isa linear relationship between a covariate and average flows, given that funds have different levels of the covariatewithin each tercile. Our empirical methodology, discussed in the next section, is designed to cleanly separate thesetwo effects. We find strong evidence of nonlinear dependence.

25

outflows are much less related to fund size among retail funds, relative to our prior finding of large

percentage outflows among the largest institutional funds.

One reading of the results from this section is that, leading up to September 17, some well-

informed investors anticipated the risks associated with the Lehman bankruptcy and began to

redeem their shares. As events unfolded, a larger swath of investors became aware of the potential

problems and responded accordingly. Conditional upon having some awareness of a broad increase

in risk in the money market, investors then needed to decide whether the risks merited withdrawing

funds. These covariates, along with lagged flows, are likely to be correlated with 1) investors’

awareness of the situation and/or 2) the extent to which broad market conditions were likely to

affect the safety of their investment with a given fund. As the week progressed, more investors

became aware, and the combination of existing redemptions and declining market liquidity increased

the potential costs associated with new redemptions. Thus, changing investor perceptions about

aggregate risks led to new withdrawals at funds which were thought to be safe earlier in the week

and additional outflows at funds which had already faced large redemptions.

5. Methodology

The empirical results presented so far provide conclusive evidence that it is important to adopt

an estimation methodology that captures heterogeneity in the cross-sectional distribution of fund

flows in order to properly infer the drivers of run-like behavior in some money funds during the

crisis. In this section we discuss how we accomplish this.

As a motivating example, imagine that a fund-specific sunspot mechanism determines how in-

vestors select between a “good” and a “bad” equilibrium. In the good equilibrium, flows simply

reflect investors’ asset allocation decisions, and the actions of other investors may be largely ir-

relevant. If investors have no reason to suspect that the fund’s portfolio is particularly risky, its

yield simply captures equilibrium risk-return tradeoffs associated with a well-diversified portfolio.

Investors’ idiosyncratic responses to changes in the investment opportunity set may be hard to

predict using current yields. However, if the bad equilibrium materializes and strategic comple-

mentarities become relevant, investors may be less aggressive in pulling their money from a fund

26

with a relatively low yield if there is a lower perceived risk of the fund breaking the buck in the

near future. Therefore, we might expect a variable like yield to be much more important for pre-

dicting magnitudes of flows for hard-hit funds. Even if the sunspot variable is unobservable, as

we move from the left to right in the cross sectional flow distribution on a given date, it is more

likely that we are observing funds which experience the good equilibrium. So, the afore-mentioned

mechanism would suggest that flows in the left tail of the cross-sectional distribution (i.e. large

outflows) should be more sensitive to yield relative to flows in the right tail of the distribution (i.e.

inflows).22 Our model specifically allows for this possibility.

Theories of bank runs differ in terms of how large a role they attribute to fundamentals versus

simple common shocks, so we focus on directly modeling cross-sectional differences. As a simple

way to capture differences between (common) shocks to all funds versus shocks specific to funds in

the left versus right tail of the cross-sectional distribution, we focus on modeling three conditional

quantiles, namely the 10th, 50th (median) and 90th quantiles. Moreover, to facilitate interpretation

of the results, we anchor the model on the effect of common shocks that affect the location (median)

of the return distribution. We then add (or subtract) spreads that quantify the difference between

the effect of covariates on funds in the left or right tails of the cross-sectional distribution of fund

flows. Recall that, in Figure 7, we found that when moving from the bottom to the top tercile

decreased the median outflow, the spread between the median and 10th percentile often increased

as well. We first look at the effect of covariates on the median, then separately consider additional

effects, if any, on these spreads. In this way, we can determine whether fundamentals, and non-

fundamentals, differentially affect funds in different parts of the cross-sectional distribution. We

also allow the relationship between covariates and flows to change over time. For example, perhaps

investors put a heavy weight on the riskiness of a fund’s holdings during the early stages of a crisis,

but place less weight during later stages–after risky funds have exhibited substantial outflows.

Our econometric approach emphasizes time-varying dynamics in fund flows as well as cross-

sectional heterogeneities in fund flows. These two features turn out to be an important part of

our data and, so, must be accommodated in the empirical analysis. Conventional approaches such

22Alternatively, the distribution of flows may be independent of yield in each equilibrium, but the probability of thebad equilibrium occurring may be increasing in yield. This alternative structure can also deliver the same predictionunder some additional assumptions.

27

as (panel) OLS fail to fully account for these effects, so we adopt an alternative methodology.

Following the study by Koenker and Bassett (1978), a variety of methods have been developed

for estimating conditional quantiles. Given a dependent variable, Yi,t, and a set of conditioning

variables, Xi,t, we can write

Yi,t = f(Xi,t, β) + εi,t, (2)

replacing the usual regression restriction that E[εi,t|Xi,t] = 0 with the restriction that P [εi,t <

0|Xi,t] = α, for some α ∈ (0, 1). As we change α, we change the probability index associated

with the quantile of interest. If α = 0.5, this is a model for the conditional median, and the key

identifying assumption is that the median of εi,t conditional on Xi,t is zero. If α = 0.1, (2) is a

model for the 10th percentile conditional on Xi,t.

To incorporate multiple quantiles in a way that focuses on the spreads (“quantile spacings”)

between the median fund versus left and right tail funds, we adopt the following specification:

Yi,t = f0(Xi,t, β) = X ′i,tβ0 + ε0i,t P [ε0i,t < 0|Xi,t] = 0.5

Yi,t = f1(Xi,t, β) = X ′i,tβ0 − exp[X ′i,tβ1] + ε1i,t P [ε1i,t < 0|Xi,t] = 0.1 (3)

Yi,t = f2(Xi,t, β) = X ′i,tβ0 + exp[X ′i,tβ2] + ε2i,t P [ε2i,t < 0|Xi,t] = 0.9.

Note that, in this model, β0 captures the common effect of X-variables that affect all funds; β1

captures the additional effect of shocks that only affect the left tail of the distribution−in our case,

those funds most strongly affected by outflows; finally, β2 captures the additional effect of shocks

that only affect the right tail of the distribution, i.e., those funds with the smallest outflows/largest

inflows. Three quantiles is the minimum number which is still sufficient to identify the degree of

heterogeneity and asymmetry in the flow distributions, both of which are likely to be empirically

relevant. We follow the convention in the literature by assuming that the median can be modeled as

a linear function of Xi,t, but treat the other quantiles in a different manner. Specifically, since the

exponential function is always non-negative, our specification ensures that β1 captures the effect

of covariates in the left tail, while β2 captures the effect of covariates in the right tail and also

guarantees that the conditional quantiles never cross.

28

An alternative way to write the data generating process for Yi,t is

Yi,t = X ′i,tβ0 −Di,t exp[X ′i,tβ1]ηi,t + (1−Di,t) exp[X ′i,tβ2]ηi,t, (4)

where ηi,t is a nonnegative random variable with P [ηi,t < 1|Xi,t] = 0.8 and Di,t is a Bernoulli

random variable which equals 1 with probability 0.5.23 If β1 = β2, this is consistent with a simple

mean-variance model where the variance is a loglinear function of Xi,t. When β1 6= β2, these

are analogous to “semi-variances” instead, where β1 and β2 governs the variance of bad and good

shocks, respectively. The latter way of writing the DGP also mirrors the manner in which we

estimate the relevant parameters.

Following the method in Schmidt (2012), we sequentially estimate the parameters of interest

using a series of standard linear quantile regressions. Specifically, we first estimate β0 using standard

linear quantile regression. Next, we estimate β1 and β2 by splitting the sample into two halves based

on the signs of the residuals and performing an additional linear quantile regression on the log of

these residuals. Using the positive residuals, we can estimate β2. To see why this works, note that

if Yi,t − X ′i,tβ0 > 0, Yi,t − Xi,tβ0 = exp[X ′i,tβ2]ηi,t. Taking logs, we get that log[Yi,t − X ′i,tβ0] =

X ′i,tβ2 + log ηi,t. Given our assumption that P [ηi,t < 1|Xi,t] = P [log ηi,t < 0|Xi,t] = 0.8, the

transformed model satisfies the standard assumptions for linear quantile regression with α = 0.8.

To get feasible estimators, β0 is replaced with β0, the initial estimate from the quantile regression

for the median. An analogous procedure works for the negative residuals, enabling us to estimate

β1. We calculate standard errors using simple bootstrap procedures.24

Our model for conditional quantiles has an additional interpretation which is particularly useful

in a panel context. Imagine that

Yi,t = X ′i,tβ0 + α0,t −Di,t exp[X ′i,tβ1 + α1,t]ηi,t + (1−Di,t) exp[X ′i,tβ2 + α2,t]ηi,t, (5)

23To see this, note that if P (ηi,t < 1|Xi,t) = 0.8, P (Yi,t < X ′i,tβ0 − exp[X ′i,tβ1]|Xi,t) = P (Di,t = 1|Xi,t)× P (ηi,t >1|Xi,t) = 0.5× (1− 0.8) = 0.1.

24Bootstrap procedures, when applicable, are generally thought to be more reliable than asymptotic approximationsin quantile regression applications. We use a clustered bootstrap, where we construct our bootstrap data by drawingcomplete fund time series with replacement, which allows for arbitrary serial correlation in the residuals within funds.

29

where (α0,t, α1,t, α2,t)′ is a vector of unobserved, time-specific shocks. α0,t is a shock which hits all

funds, regardless of whether they receive a good or a bad shock. α1,t scales up or down the variance

of the bad shock, while α2,t scales up or down the variance of the good shock. Hence, β1 and β2

have useful factor-loading interpretations: If X ′i,tβ1 = 0, the variance of the fund’s bad shock is the

aggregate shock; If X ′i,tβ1 > 0, the sensitivity to the aggregate shock increases. This specification

makes sense in our application given that certain fund-specific exposures only matter on days where

investors are concerned about them. Again, implementation is straightforward, since these effects

can be included by augmenting the Xi,t vector with time dummy variables.

This econometric model gives us a picture of how the conditional flow distributions evolve

without imposing distributional assumptions on tail behavior. However, if we add a simple distri-

butional assumption on ηi,t, we can use the non-crossing property to generate internally consistent

density/distribution estimates by interpolating between quantiles. This interpolation adds little in-

formation if we want to look at flow distributions over a single day, but it can be useful for looking

at multi-period cumulative flow distributions. This turns out to be important, since covariates and

lagged flows are not independent from one another. For example, imagine that outflows prior to

September 17 were highly sensitive to portfolio risk, but only depended on lagged flows thereafter.

In that case, even if the “direct effect” of portfolio risk variables is zero on September 17 (i.e. the

slope coefficients are zero on that date), there is still an “indirect effect” of portfolio risk which

operates through lagged flows. Therefore, we would still expect funds with more portfolio risk to

have bigger outflows on September 17. These indirect effects can be substantial. We will discuss

these multi-period simulations in greater detail in the following section.

6. Estimation Results

This section presents more formal estimation results. We start off with conventional OLS panel

estimation results and then show how these change when we consider our more flexible quantile

regression model.

30

6.1. OLS Panel Estimates

So far, we have focused our analysis on differences in the flows experienced by individual funds as

well as differences in the dynamics of outflow, i.e., the degree of persistence, observed across funds.

In this section, we more formally estimate models that attempt to identify the characteristics of a

fund that may make it more susceptible to run-like behavior by investors.

As a first cut, and for later comparison, Table 5 presents OLS panel estimates of fund-level

flows as a function of the covariates described in a prior section, in addition to lagged fund flows,

measured relative to the lagged average fund flows (computed as a value-weighted cross-sectional

average), yit−1 − yt−1, and divided into above-average lagged flows, yit−1 − yt−1 > 0, and below-

average lagged flows, yit−1 − yt−1 < 0. By separately considering the effect of flows above and

below the cross-sectional average, we can see if persistence of fund flows differs among funds with

the (relatively) largest inflows or outflows.

Our specification is, thus,

yit = αt + β1 max(yit−1 − yt−1, 0) + β2 min(yit−1 − yt−1, 0) + β3AV GY IELDit +

β4EXPRit + β5FLOWSTDEVit + β6PIPERCit + β7LOGTNAit + εit.

We present results separately for the pre-crisis period (September 10-16, 2008), the crisis period

(September 17-19), and the “post-crisis” period (September 22-October 17).25 The run on the

money market funds greatly intensified between September 17 up to September 19, the date where

the Federal Reserve announced its backstop for mutual funds. The pre-crisis period coincides with

the beginning of leakage of money from the funds leading up to the Lehmann crisis. The post-crisis

period ends on October 17. This analysis allows us to address whether fund flow dynamics were

altered in interesting ways during the crisis.

As shown in Table 5, the AR(1) coefficient indicates that persistence in institutional fund flows

(both inflows and outflows) was quite strong during the pre-crisis period–a persistence coefficient

25Although Lehman declared bankruptcy on September 15, the first major impact of the crisis on money funds wason September 17. The “post crisis” period is not one that we consider to be free from crisis effects, but it is distinctfrom the crisis week because of the announced federal guarantee programs.

31

of 0.3169 is observed. This finding is evidence of institutions selectively piling into certain funds,

perhaps as they left money funds that were not registered 1940-Act funds in a flight to safety. In

addition, institutions selectively piled out of other funds. The AR(1) coefficient is much weaker

during the crisis period for outflows, which is presumably evidence that there was not a simple

amplification effect operating during the crisis (we will revisit this finding in our next section).

During the post-crisis period, outflows exhibit some amplification effects.

Next, consider the effect of fund characteristics on flows. Most of the fund characteristics exhibit

significant coefficients during the crisis. During this period, funds with higher weighted-average

portfolio yields experience greater outflows, as shown by our graphical results of Figure 8. Again,

this is likely a result of investor concerns that funds with higher yields are riskier and, thus, more

prone to runs than funds with a lower yield. In contrast, funds with higher expense ratios experience

smaller outflows during and after the crisis, likely due to a clientele effect; investors in funds with

higher expense ratios are likely to be less sophisticated and perhaps in less need of liquidity than

investors in funds with lower expense ratio. Funds belonging to complexes with a higher percentage

of institutional (money fund) investors also experience higher outflows, and the largest funds are

most exposed to outflows. Flow volatility does not significantly affect flows, although this variable

is highly correlated with the yield variable.

Specifications that do not include lags of the dependent variable (flows) exhibit coefficients for

exogenous variables that are similar to those that include lags. Thus, investors react to lagged

flows in addition to their reaction to fundamentals.

To summarize, fund characteristics (“fundamentals”) clearly matter for institutional fund flows,

particularly during the crisis period. Very different results emerge for the prime retail funds. In

sharp contrast with the prime institutional funds, there is negative persistence in the flows to the

prime retail funds, which is likely due to the normal movement of money into and out of money

funds as a temporary holding while transitioning a portfolio or liquidating all or part of a portfolio

of stocks or bonds. Moreover, no characteristic seems to matter greatly for prime retail funds other

than fund size which again affects flows negatively so that larger funds experienced the greatest

outflows, particularly during the crisis.

32

6.2. Quantile Regressions

The OLS regression results in Table 5 ignore any heterogeneity across funds in investor persistence

in outflows, or the impact of fund characteristics on investor outflows. However, our earlier results in

Figures 5-8 show that such heterogeneity is, in fact, important. These non-parametric plots show

that outflows are especially persistent among a minority of funds having certain characteristics.

This evidence suggests that we should adopt a regression approach that allows for differences in

the effect of covariates across the distribution of funds.

To this end, Table 6 presents fund-level panel quantile regressions. Column 1 shows coeffi-

cient estimates, and p-values (beneath the point estimates), for the model over the February 1

to September 9, 2008. This period is used as a “normal period,” against which to compare the

pre-crisis (September 10-16), crisis (September 17-19), and “post-crisis” (September 22-October

17) periods.

First consider the effect of a common shock which, according to our model (4), affects the

entire distribution of fund flows. The persistence in the effect of a common shock is more than

double that of funds experiencing above-average outflows (yit−1 − yt−1 < 0) than for funds with

above-average inflows (yit−1− yt−1 > 0), particularly during the crisis (a first-order autocorrelation

of 0.57 versus 0.23), but also during the pre-crisis period (0.24 versus 0.12). In contrast, there is

very little persistence in the effect of prior-day flows on a common shock during the post-crisis.

Turning to the coefficients on fund and investor characteristics, Table 6 shows that higher yields

are associated with larger outflows during the crisis. However, this impact is small for the common

shock (entire distribution) effect: a one standard deviation increase in yield is associated with a 64

basis point increase in log daily outflows during the crisis days. Yields also only marginally affect

flows during the pre- and post-crisis periods. Two other fund characteristics are also associated

with higher outflows, predominantly during the crisis: (1) funds with lower expense ratios and (2)

funds that are larger in total net assets under management. Note that higher lagged flow standard

deviation is also associated with crisis-period outflows, although the coefficient is only marginally

significant (with a p-value of 11.7%). In general, the results for the common shock component are

consistent with those from the panel OLS estimation of Table 5.

33

Further insights can be gained from examining the effects of covariates on the left- and right-

tails, which are shown in the middle and bottom panels, respectively. First, note that the lagged

standard deviation of daily flows, FlowSTDEV , exhibits a large effect (which is also statistically

significant) on funds in both tails of the daily flow distribution. A higher flow standard deviation

has a greater negative impact on flows in the left tail, and a greater positive impact on flows in

the right tail, during all periods.26 This finding reflects that high lagged flow standard deviation

persists, and that these funds are more likely to end up in the tails of the flow distribution (a “hot

money” effect on certain funds).

In contrast, the coefficients on expense ratio, EXPR, are negative in the tails, meaning that

lower expense ratio funds indeed exhibit more dramatic outflows (left tail) or inflows (right tail)–

again, a tendency for hot money to prefer these types of funds. Interestingly, the level of institu-

tional money managed (in money market mutual funds) at the complex level has an asymmetric

effect on the tails. Funds that are a part of complexes with higher levels of institutional money

have greater left tail exposure (i.e., large outflows) during the crisis, whereas this variable has little

effect on the right tail. This finding indicates that money did not generally flow from high institu-

tional money complexes to low institutional money complexes. This finding is also consistent with

Figure 1, which indicates that outflows from prime institutional funds (especially those that were

part of complexes with a large proportion of institutional funds) flowed to government institutional

money market funds. Higher average yields, AV GY IELD, have a (negative) impact on funds in

the left tail, as shown by the left-tail coefficient of 0.23. A one standard deviation increase in yield

is increases the fund’s exposure to a bad shock by 23%.

Yield has an increased impact on the right tail, too, with a coefficient of 0.11. Finally, there is

evidence that fund size has a greater impact on flows in the flow tails during the crisis days, but

less impact during the post-crisis days.

Table 7 repeats the analysis for prime retail funds. There is no significant evidence of persistence

in the effect of common shocks on retail fund flows before or after the crisis, while above-average

flows are mildly persistent during the crisis. Higher yields are associated with bigger outflows during

26Note that the positive coefficient of 0.35 during the crisis days in the left tail corresponds to a negative relationbetween lagged flow standard deviation and current day flows, as specified in Equation (3), while a positive coefficientof 0.45 corresponds to a positive relation in the right tail.

34

the crisis, but do not differentially affect the tails (unlike our results noted above for institutional

shareclasses). Also, funds with higher expense ratios see smaller outflows before and after the crisis,

although this variable had no effect during the crisis, indicating that some retail investors may have

been more sophisticated than others. Once again, larger funds experience greater outflows.

Flow volatility has a much higher impact on the left and right tails of the retail flow distribution,

both before and after the crisis, though not during the crisis. Interestingly, right-tail (inflows)

exhibit substantial persistence prior to the crisis with a coefficient of 0.30, but do not exhibit

significant persistence during the crisis days.

Figure 9 presents plots of the common shocks from the panel OLS vs. quantile regressions for

both institutional and retail funds. For the common shocks to the distributions (ξ0,t in Equation

(5), and shown in the graphs on the left of the figure), there are large similarities between the OLS

and quantile regressions, such as the large outflow shocks that affect all prime institutional funds

during September 17-19. However, the additional shocks to the tails (ξ1,t and ξ2,t in Equation

(5), and shown in the graphs on the right of the figure), estimated by the quantile regressions,

show that these shocks to the tails (which are ignored by the OLS analysis) are as important

as (i.e., of the same magnitude) or larger than the common shocks to the distribution of flows.

That is, the quantile regressions expose the very uneven level of flow shocks that occur across the

distribution–shocks have a much larger impact on the tails, especially the left tail.

Figure 10 provides a different view of the effect that flows have at the peak of the crisis on

September 17, 2008. The graphs plot the effect of changing the average gross yield in a range

between 2.4% and 3.4% on log flows, and can be viewed as an elasticity of flows with respect to

changes in portfolio yields. We can clearly see that the effect of increasing portfolio yield from its

mean by one standard deviation is very minor for funds in the right tail of the distribution, i.e.,

funds with the largest inflows (smallest outflows). A somewhat bigger effect (but still relatively

mild) of raising the yield by one standard deviation is seen on the median fund, which experiences

a greater outflow as a result of the higher yield. By far the largest effect is seen in the left tail

of the flow distribution, which shifts markedly to the left in the flow distribution as a result of an

increase in yield.

35

To summarize our results of this section, we find strong evidence of persistence in flows (espe-

cially outflows) in prime institutional funds during the crisis period as well as a significant negative

impact of portfolio average yield on flows. Persistence in flows (first-order autocorrelation) impact

the entire distribution similarly (i.e., tail effects are not significantly different), while higher yield

pushes funds in the left tail of the flow distribution to even more extreme levels of outflows. Flow

volatility also has a significantly different impact on the tails of the flow distribution, with higher

flow volatility pushing funds further into the tails. Expense ratio also has a more muted effect,

but still affects the tails differentially from the median of the distribution. Finally, fund size affects

mainly the tails, and does so in an asymmetric way (the right tail is more impacted by fund size).

For prime retail funds, we find almost no significant predictors of flows during the crisis days, but

some post-crisis persistence in the left tail (consistent with retail investors reacting slowly to the

news of the money market fund crisis that developed in institutional funds).

6.3. Dynamics in Flows Following a Shock

Figure 11 graphs forecasts of fraction flows, for the tails and for the median fund, as provided by

our fitted quantile model of the prior section for institutional funds. Here, we illustrate the (fitted)

flow dynamics following a common shock to flows of -10% (i.e., an outflow of 10%), measured

relative to the average fund flow, or a shock to flows of +10% (an inflow relative to the average

fund). As a baseline reference, the middle graph in Figure 11 shows that only the right tail of

prime institutional funds experience positive flows conditional on prior fund flows being set at their

median value. Moreover, as shown in the left graph, when funds are hit with a 10% outflow shock,

the median fund experiences far greater net outflows than we would expect if only the location of

the fund flow distribution shifted, i.e., in the absence of heterogeneity in the effect of past flows on

current flows.

To simulate the impact of perturbations in the value of exogenous variables on multi-period

flow distributions, we note from Figure 12 that the standardized residual density from the baseline

model for institutional funds is very closed to a properly parameterized Laplace distribution. We,

therefore, draw shocks from this parametric distribution in the multi-period simulations.

36

Results from this exercise are shown in Figure 13. At the beginning of each window (September

10), we fix each of the explanatory variables at its average plus or minus one standard deviation.

The results show that there are long-lasting effects of changes to many covariates, especially for

funds with the greatest left tail exposure. Most notably, reducing the expense ratio or increasing

the volatility by one standard deviation leads to a cumulative outflow that approaches 40% by

September 26, 2008, relative to the baseline scenario, while increasing the average yield, fund size,

or the percentage of institutional investor money by one standard deviation is associated with

additional cumulative outflows of roughly 30%. Most importantly, note the asymmetry in the

impact of the covariates on flows: a one standard deviation increase, for all variables, exhibits quite

different outflow impacts than a one standard deviation decrease.

7. Robustness Analysis and Extensions

Table 8 presents results when the asymmetry in the autoregressive effect of flows is measured around

zero, versus the alternative of centering at the average flow, yt−1 = 0, that was used in Table 6.

The effect of this change, shown in Table 8, alters the autoregressive dynamics somewhat, although

their relative magnitudes are similar to those in Table 6, while the coefficients for the covariates do

not change much, in most cases.

Table 9 extends Table 6 to allow for cross-category dynamics in the fund-level panel quantile

regressions, principally by including variables MULTCAT and LAGOTHER. MULTCAT is an

indicator (dummy) variable that equals 1 only if a money market fund has both prime institutional

and prime retail funds. This variable captures whether investors view funds with a mixed investor

base differently. For example, a run imposed by institutional investors may result in retail investors

holding the remaining illiquid assets–which results in a run among retail investors.

The coefficient on LAGOTHER, where the dependent variable is daily prime institutional (re-

tail) fund flows, captures the effect of lagged prime retail (institutional) fund flows. The largest

effect is seen among prime institutional funds residing in the left tail of flows during the crisis.

For these funds, a large and highly significant effect of lagged retail flows of -0.2457 is estimated.

The coefficient on MULTCAT does not indicate any consistent effect of mixed investors on flows of

37

either investor class. For the simpler OLS panel regressions shown in Table 10, the effect of lagged

retail flows on prime institutional flows is positive and highly significant during the crisis.

8. Conclusion

This paper studies money fund runs during the crisis period of September and October 2008. We

find that correlated withdrawals were especially pronounced among money funds that catered pre-

dominantly to institutional investors, although we also find weak evidence of correlated withdrawals

among retail money funds during the crisis. This finding indicates that “sophisticated” investors

mainly present a “bank-run” risk (a negative externality) to each other, and present a weaker risk

to “passive” (retail) investors in same-complex money funds.

Second, we find that runs were more pronounced among funds that had less liquidity, in terms of

their lower holdings of securities that matured with seven days. We also find that money fund runs

were more likely when the fund didn’t have “deep pocket backing,” indicating that investors infer

that funds are guaranteed by their management company. And, finally, institutional investors, for

the most part, moved their money into U.S. government (only) money market funds in the same

fund complex, indicating that they were sophisticated enough to know when not to rely on the

management company’s liquidity-provision.

38

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40

 

Table 1: Total Net Assets and Number of Shareholder Accounts (year-end)

Total net assets Number of shareholder accounts*

(billions of dollars) (thousands)

Taxable Tax-exempt Taxable Tax-exempt Year Total Government Prime Total Government Prime 1990 $498.3 $107.6 $307.1 $83.6 22,969 2,273 19,305 1,391 1991 542.4 138.0 314.6 89.9 23,556 2,547 19,316 1,693 1992 546.2 151.3 300.1 94.8 23,647 2,817 18,954 1,876 1993 565.3 148.7 313.2 103.4 23,585 2,806 18,780 1,998 1994 611.0 147.1 353.5 110.4 25,383 3,049 20,295 2,039 1995 753.0 179.2 450.8 123.0 30,144 3,824 24,035 2,285 1996 901.8 217.9 544.1 139.8 32,200 4,147 25,760 2,292 1997 1058.9 245.0 653.1 160.8 35,624 4,548 28,413 2,663 1998 1351.7 302.8 860.4 188.5 38,847 4,384 32,058 2,405 1999 1613.1 325.9 1082.8 204.4 43,616 4,793 36,385 2,438 2000 1845.2 352.5 1254.7 238.0 48,138 4,888 40,592 2,659 2001 2285.3 437.2 1575.7 272.4 47,236 5,124 39,290 2,821 2002 2272.0 447.7 1549.5 274.8 45,380 5,092 37,634 2,655 2003 2052.0 403.5 1360.1 288.4 41,214 4,111 34,301 2,802 2004 1913.2 372.4 1230.4 310.3 37,636 3,651 31,143 2,842 2005 2040.5 382.5 1324.0 334.0 36,837 3,117 30,916 2,805 2006 2338.5 405.8 1566.2 366.4 37,067 3,292 30,714 3,061 2007 3085.8 726.1 1894.6 465.1 39,130 3,481 32,181 3,467 2008 3832.2 1450.3 1890.4 491.5 38,112 4,160 30,339 3,613

Source: ICI Factbook, 2009

   

 

Table 2: Number of Funds and Number of Share Classes

(Year-end)

Number of funds Number of share classes

Taxable Tax-

exempt

Taxable Tax-

exempt Year Total Government Prime Total Government Prime 1990 741 174 332 235 762 180 343 239 1991 820 211 342 267 871 228 364 279 1992 864 234 351 279 914 247 369 298 1993 920 260 368 292 1,009 280 393 336 1994 963 271 375 317 1,261 360 493 408 1995 997 279 395 323 1,380 394 555 431 1996 988 271 395 322 1,453 404 596 453 1997 1,013 271 411 331 1,549 428 642 479 1998 1,026 275 410 341 1,627 459 674 494 1999 1,045 284 418 343 1,730 493 733 504 2000 1,039 277 426 336 1,855 531 793 531 2001 1,015 273 416 326 1,948 575 822 551 2002 989 265 414 310 2,006 588 876 542 2003 974 258 404 312 2,031 584 879 568 2004 943 247 392 304 2,046 589 882 575 2005 871 220 375 276 2,031 565 900 566 2006 847 212 362 273 2,012 567 888 557 2007 805 197 349 259 2,018 553 897 568 2008 784 195 341 248 1,989 568 880 541 *Number of shareholder accounts includes a mix of individual and omnibus accounts. Note: Data for funds that invest primarily in other mutual funds were excluded from the series. Components may not add to the total because of rounding. Source: ICI Factbook, 2009

   

 

Table 3: Net New Cash Flow1 and Components of Net New Cash Flow of Money Market Funds ($Billions/year)

Sales Redemptions

Year Net new cash flow New + exchange New2 Exchange3 Regular + exchange Regular4 Exchange5

1984 $35.1 $640.0 $620.5 $19.5 $604.9 $587.0 $18.0

1985 -5.3 848.5 826.9 21.6 853.7 831.1 22.7

1986 33.6 1026.7 978.0 48.7 993.2 948.7 44.5

1987 10.1 1147.9 1049.0 98.8 1137.8 1062.7 75.1

1988 0.1 1130.6 1066.0 64.6 1130.5 1074.3 56.2

1989 64.1 1359.6 1296.5 63.2 1295.5 1235.5 60.0

1990 23.2 1461.5 1389.4 72.1 1438.4 1372.8 65.6

1991 6.1 1841.1 1778.5 62.6 1835.1 1763.1 72.0

1992 -16.0 2449.8 2371.9 77.8 2465.8 2383.0 82.8

1993 -13.9 2756.3 2666.0 90.3 2770.2 2673.5 96.7

1994 8.5 2725.2 2586.5 138.7 2716.7 2599.4 117.3

1995 89.4 3234.2 3097.2 137.0 3144.8 3002.0 142.9

1996 89.4 4157.0 3959.0 198.0 4067.6 3868.8 198.8

1997 103.5 5127.3 4894.2 233.1 5023.9 4783.1 240.8

1998 235.5 6407.6 6129.1 278.4 6172.1 5901.6 270.5

1999 193.7 8081.0 7719.3 361.6 7887.3 7540.9 346.4

2000 159.4 9826.7 9406.3 420.4 9667.3 9256.4 411.0

2001 375.3 11737.3 11426.8 310.5 11362.0 11065.5 296.5

2002 -46.5 12035.8 11739.6 296.2 12082.2 11810.7 271.5

2003 -258.4 11235.9 11011.3 224.6 11494.3 11267.7 226.6

2004 -156.6 10953.4 10786.9 166.5 11110.0 10939.7 170.3

2005 63.1 12596.5 12420.4 176.1 12533.4 12362.6 170.8

2006 245.2 15707.3 15496.0 211.3 15462.0 15269.4 192.6

2007 654.5 21315.2 21040.1 275.1 20660.7 20409.4 251.3

2008 636.8 24574.2 24189.1 385.1 23937.3 23620.7 316.7

1Net new cash flow is the dollar value of new sales minus redemptions, combined with net exchanges. 2New sales are the dollar value of new purchases of mutual fund shares. This does not include shares purchased through reinvestment of dividends in existing accounts. 3Exchange sales are the dollar value of mutual fund shares switched into funds within the same fund group. 4Redemptions are the dollar value of shareholder liquidation of mutual fund shares. 5Exchange redemptions are the dollar value of mutual fund shares switched out of funds and into another fund in the same group. Note: Data for funds that invest primarily in other mutual funds were excluded from the series. Components may not add to the total because of rounding. Source: ICI Factbook, 2009

 

Table 4: Asset Composition of Taxable Non-Government Money Market Funds as a Percent of Total Net Assets (Year-End)

Year

Total net assets

(billions of

dollars)

U.S. Treasury bills %

Other Treasury securities

%

U.S. government

agency issues %

Repurchase agreements

%

Certificates of deposit

% Eurodollar

CDs % Commercial

paper % Bank

notes1 % Corporate notes2 %

Other assets3

%

Average maturity

(days)

1990 $307.1 4.3 2.3 4.7 3.3 6.8 8.8 65.0 – – 4.7 48 1991 314.6 5.8 3 4.3 4.1 10.5 6.9 59.7 – – 5.8 56 1992 300.1 2.8 2.6 7.5 5.1 10.3 6.7 57.4 – – 7.4 59 1993 313.2 2.7 2.5 11.9 6.4 7.9 3.2 52.2 – – 13.2 58 1994 353.5 2.6 1.3 11.3 6 6.3 4.5 53.2 2.4 – 12.4 38 1995 450.8 1.5 1 9.2 6.5 8.8 4.4 52.2 3.7 – 12.6 60 1996 544.1 0.7 1.9 8.9 5.4 12.7 4.3 50.5 2.3 – 13.3 56 1997 653.1 0.5 0.8 5.5 5.6 14.6 3.7 51.5 3.2 – 14.8 57 1998 860.4 0.6 0.9 9.5 4.9 12.9 3.6 48.3 3.9 5.8 9.6 58 1999 1082.8 0.5 0.3 6.7 5.1 12.7 3.9 48.9 3.1 8.3 10.4 49 2000 1254.7 0.5 0.1 6.2 4.5 10.2 7.8 50.2 3.6 10.4 6.6 53 2001 1575.7 0.6 0.3 12.4 6.4 13.2 8.8 41.2 1.5 10.9 4.6 58 2002 1549.5 1.5 0.3 12.1 8.4 12.4 8.2 39.7 1.3 11.8 4.3 54 2003 1360.1 1.5 0.4 15.2 8.5 10.5 6 35.1 2 15.9 4.7 59 2004 1230.4 0.5 0.1 12.2 9 12.7 6.9 33.4 2.6 17.6 5 41 2005 1324.0 0.8 0.1 4.6 12.4 13.3 6.9 37.9 2.3 17.6 4.2 37 2006 1566.2 0.2 0.2 3 10.9 12.5 5.5 39 2.2 21.3 5.3 49 2007 1894.6 0.9 0.2 3.3 12.4 13.4 6.9 36.2 3.9 16.4 6.4 44 2008 1890.4 2.4 0.5 13.1 9.1 18.6 7 33.4 3 9.1 3.8 47

1Prior to 1994, bank notes are included in other assets. 2Prior to 1998, corporate notes are included in other assets. 3Other assets include banker's acceptances, municipal securities, and cash reserves. Note: Data for funds that invest primarily in other mutual funds were excluded from the series. Source: ICI Factbook, 2009

Panel A: Prime Institutional FundsCoefficients with Lags Coefficients without Lags

Variable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 -0.0444 0.3169 0.2344 -0.0402 - - -

[0.002] [0.017] [0.069] [0.158] - - -yi,t−1 − yt−1 < 0 -0.0175 0.2831 0.0606 0.1059 - - -

[0.339] [0.054] [0.416] [0.005] - - -AV GY IELD 0.0001 -0.0011 -0.0119 -0.0006 -0.0012 -0.0130 -0.0007

[0.341] [0.109] [0.001] [0.249] [0.159] [0.002] [0.234]EXPR -0.0001 0.0012 0.0083 0.0016 0.0011 0.0097 0.0019

[0.336] [0.185] [0.018] [0.016] [0.313] [0.025] [0.009]FLOWSTDEV 0.0013 -0.0025 -0.0035 0.0002 -0.0028 -0.0041 -0.0004

[0.000] [0.061] [0.158] [0.415] [0.083] [0.113] [0.378]PIPERC -0.0003 -0.0031 -0.0070 -0.0009 -0.0036 -0.0083 -0.0010

[0.083] [0.051] [0.037] [0.080] [0.048] [0.035] [0.085]LOGTNA 0.0005 -0.0059 -0.0209 -0.0011 -0.0075 -0.0255 -0.0010

[0.001] [0.000] [0.000] [0.138] [0.000] [0.000] [0.145]Panel B: Prime Retail Funds

Coefficients with Lags Coefficients without LagsVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 -0.4277 -0.5703 -0.3988 -0.2841 - - -

[0.000] [0.039] [0.096] [0.041] - - -yi,t−1 − yt−1 < 0 -0.1075 -0.2206 -0.5006 -0.1331 - - -

[0.085] [0.183] [0.317] [0.117] - - -AV GY IELD 0.0004 0.0017 -0.0003 -0.0003 0.0012 -0.0013 -0.0002

[0.035] [0.290] [0.383] [0.283] [0.264] [0.228] [0.320]EXPR -0.0002 -0.0001 0.0012 0.0008 0.0003 0.0004 0.0007

[0.150] [0.507] [0.422] [0.214] [0.347] [0.450] [0.192]FLOWSTDEV 0.0035 0.0049 0.0024 0.0007 0.0016 -0.0004 0.0001

[0.004] [0.127] [0.393] [0.318] [0.252] [0.444] [0.416]PIPERC -0.0001 0.0005 0.0029 0.0004 -0.0004 0.0041 0.0004

[0.313] [0.432] [0.326] [0.281] [0.445] [0.320] [0.268]LOGTNA -0.0003 -0.0027 -0.0069 -0.0023 -0.0014 -0.0065 -0.0018

[0.294] [0.137] [0.004] [0.002] [0.179] [0.000] [0.000]

Table 5: Fund-Level Panel Regressions - OLS Coefficients

This table presents the coefficients from an OLS regression with time dummies, where the slope coefficients vary over

three different periods in 2008: 9/10-9/16, 9/17-9/19, and 9/22-10/17, respectively. Time dummies are omitted for

brevity. Numbers in brackets are one-sided bootstrapped p-values clustered at the fund level.

Common ShockVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 -0.0270 0.1124 0.2260 -0.0125

[0.070] [0.019] [0.014] [0.345]yi,t−1 − yt−1 < 0 -0.0057 0.2369 0.5761 0.0796

[0.335] [0.032] [0.043] [0.005]AV GY IELD 0.0002 -0.0007 -0.0064 -0.0007

[0.128] [0.080] [0.000] [0.022]EXPR -0.0000 0.0009 0.0033 0.0012

[0.348] [0.049] [0.054] [0.003]FLOWSTDEV 0.0001 -0.0002 -0.0040 -0.0011

[0.284] [0.343] [0.117] [0.086]PIPERC -0.0002 -0.0006 -0.0002 -0.0005

[0.066] [0.206] [0.365] [0.194]LOGTNA 0.0002 -0.0026 -0.0133 -0.0013

[0.019] [0.000] [0.000] [0.016]

Left Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 0.1064 -0.0211 0.0038 0.0545

[0.000] [0.405] [0.572] [0.003]|yi,t−1 − yt−1 < 0| 0.0693 0.0369 -0.0154 0.0819

[0.000] [0.106] [0.284] [0.003]AV GY IELD -0.0217 0.1283 0.2314 0.0490

[0.334] [0.080] [0.095] [0.238]EXPR -0.1978 -0.5379 -0.2021 -0.2998

[0.012] [0.004] [0.144] [0.002]FLOWSTDEV 0.5162 0.4453 0.3451 0.3044

[0.000] [0.003] [0.003] [0.000]PIPERC 0.0588 0.1092 0.2902 0.0818

[0.089] [0.107] [0.010] [0.106]LOGTNA -0.0040 0.0507 0.0632 -0.1031

[0.625] [0.188] [0.161] [0.063]

Right Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 0.0108 0.1168 -0.0066 0.0591

[0.125] [0.098] [0.322] [0.068]|yi,t−1 − yt−1 < 0| 0.0595 0.0716 0.0584 0.0538

[0.000] [0.075] [0.135] [0.003]AV GY IELD -0.0229 -0.0121 0.1080 0.0090

[0.332] [0.456] [0.119] [0.559]EXPR -0.2059 -0.0918 -0.0290 -0.1761

[0.000] [0.180] [0.177] [0.043]FLOWSTDEV 0.6262 0.5556 0.4517 0.4205

[0.000] [0.002] [0.007] [0.000]PIPERC 0.0924 0.1509 0.0057 0.0486

[0.026] [0.066] [0.356] [0.407]LOGTNA 0.0551 0.0846 0.2846 -0.0374

[0.156] [0.268] [0.064] [0.312]

Table 6: Fund-Level Panel Quantile Regressions - Prime Institutional

This table presents the coefficients from estimating equation (2). β0, β1, and β2 vary over three different periods

in 2008: 9/10-9/16, 9/17-9/19, and 9/22-10/17, respectively. Time dummies are omitted for brevity. Numbers in

brackets are one-sided bootstrapped p-values clustered at the fund level. Coefficients for lagged variables have been

divided by 100 for spacings 1 and 2.

Common ShockVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 -0.1023 -0.5352 0.1693 -0.0030

[0.000] [0.138] [0.016] [0.348]yi,t−1 − yt−1 < 0 -0.0143 -0.0391 0.0382 -0.0275

[0.336] [0.470] [0.219] [0.248]AV GY IELD 0.0001 -0.0001 -0.0014 -0.0004

[0.152] [0.297] [0.060] [0.154]EXPR -0.0000 0.0006 -0.0006 0.0007

[0.263] [0.005] [0.249] [0.024]FLOWSTDEV 0.0000 0.0001 -0.0018 -0.0004

[0.394] [0.347] [0.140] [0.226]PIPERC -0.0000 -0.0001 0.0002 -0.0000

[0.277] [0.270] [0.382] [0.406]LOGTNA -0.0000 0.0000 -0.0018 -0.0015

[0.182] [0.456] [0.000] [0.000]

Left Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 0.2289 0.0330 0.0660 0.1263

[0.000] [0.299] [0.181] [0.006]|yi,t−1 − yt−1 < 0| 0.1338 0.1252 0.1325 0.1815

[0.001] [0.104] [0.141] [0.013]AV GY IELD -0.1295 -0.0678 -0.2538 -0.1005

[0.030] [0.280] [0.142] [0.216]EXPR 0.0283 -0.0635 0.0973 -0.0177

[0.257] [0.435] [0.353] [0.643]FLOWSTDEV 0.3241 0.7901 0.2404 0.3734

[0.002] [0.000] [0.262] [0.004]PIPERC 0.0977 -0.0208 0.3245 0.0564

[0.099] [0.568] [0.158] [0.275]LOGTNA -0.1738 -0.1003 0.2077 -0.0829

[0.010] [0.130] [0.118] [0.102]

Right Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 0.0956 0.3086 -0.1047 0.1491

[0.003] [0.034] [0.492] [0.028]|yi,t−1 − yt−1 < 0| 0.1104 -0.0321 0.0703 0.0674

[0.003] [0.387] [0.150] [0.057]AV GY IELD -0.1723 -0.0496 -0.1062 -0.1438

[0.002] [0.286] [0.361] [0.025]EXPR 0.0688 0.1649 0.0236 0.0688

[0.142] [0.157] [0.367] [0.166]FLOWSTDEV 0.5962 0.8579 0.0584 0.3910

[0.000] [0.022] [0.299] [0.007]PIPERC 0.0968 0.1494 -0.0870 0.0618

[0.091] [0.139] [0.364] [0.178]LOGTNA -0.1875 -0.2080 -0.3070 -0.0479

[0.008] [0.116] [0.112] [0.212]

Table 7: Fund-Level Panel Quantile Regressions - Prime Retail

This table presents the coefficients from estimating equation (2). β0, β1, and β2 vary over three different periods

in 2008: 9/10-9/16, 9/17-9/19, and 9/22-10/17, respectively. Time dummies are omitted for brevity. Numbers in

brackets are one-sided bootstrapped p-values clustered at the fund level. Coefficients for lagged variables have been

divided by 100 for spacings 1 and 2.

Common ShockVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 > 0 -0.0191 0.0739 0.0976 -0.0217

[0.113] [0.194] [0.396] [0.243]yi,t−1 < 0 -0.0182 0.2648 0.4314 0.0813

[0.111] [0.000] [0.000] [0.001]AV GY IELD 0.0002 -0.0004 -0.0062 -0.0006

[0.153] [0.104] [0.000] [0.041]EXPR -0.0000 0.0005 0.0022 0.0011

[0.414] [0.068] [0.065] [0.001]FLOWSTDEV 0.0000 -0.0001 -0.0039 -0.0010

[0.328] [0.594] [0.150] [0.116]PIPERC -0.0002 -0.0004 -0.0003 -0.0005

[0.053] [0.236] [0.384] [0.156]LOGTNA 0.0002 -0.0025 -0.0111 -0.0012

[0.021] [0.001] [0.000] [0.011]

Left Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 > 0 0.1122 0.0764 0.0259 0.0605

[0.000] [0.182] [0.408] [0.004]|yi,t−1 < 0| 0.0789 0.0680 -0.0110 0.0699

[0.000] [0.019] [0.413] [0.003]AV GY IELD -0.0232 0.1392 0.2110 0.0196

[0.292] [0.101] [0.072] [0.268]EXPR -0.2051 -0.4962 -0.2474 -0.3179

[0.010] [0.002] [0.096] [0.002]FLOWSTDEV 0.4873 0.3560 0.3061 0.2688

[0.000] [0.005] [0.029] [0.000]PIPERC 0.0558 0.1078 0.2529 0.0852

[0.089] [0.175] [0.008] [0.090]LOGTNA -0.0182 0.1009 0.1131 -0.1238

[0.562] [0.150] [0.211] [0.055]

Right Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 > 0 0.0109 0.1282 -0.0539 0.0741

[0.065] [0.144] [0.440] [0.034]|yi,t−1 < 0| 0.0672 0.0444 0.0407 0.0578

[0.000] [0.136] [0.030] [0.003]AV GY IELD -0.0272 -0.0333 0.1599 -0.0156

[0.312] [0.397] [0.140] [0.366]EXPR -0.2046 -0.0489 -0.0489 -0.1666

[0.001] [0.204] [0.175] [0.035]FLOWSTDEV 0.6113 0.5624 0.5306 0.4236

[0.000] [0.004] [0.006] [0.000]PIPERC 0.0934 0.1881 -0.0049 0.0140

[0.021] [0.046] [0.558] [0.425]LOGTNA 0.0471 0.0740 0.2408 -0.0336

[0.178] [0.333] [0.062] [0.327]

Table 8: Fund-Level Panel Quantile Regression - Prime Institutional with Alternative Lag Specification

This table presents the coefficients from estimating equation (2). β0, β1, and β2 vary over three different periods

in 2008: 9/10-9/16, 9/17-9/19, and 9/22-10/17, respectively. Time dummies are omitted for brevity. Numbers in

brackets are one-sided bootstrapped p-values clustered at the fund level. Coefficients for lagged variables have been

divided by 100 for spacings 1 and 2.

Common ShockVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 -0.0273 0.0951 0.2055 0.0012

[0.068] [0.013] [0.023] [0.617]yi,t−1 − yt−1 < 0 -0.0057 0.2390 0.5723 0.0813

[0.353] [0.039] [0.056] [0.007]AV GY IELD 0.0002 -0.0006 -0.0078 -0.0007

[0.126] [0.090] [0.000] [0.034]EXPR -0.0000 0.0007 0.0033 0.0012

[0.363] [0.052] [0.058] [0.002]FLOWSTDEV 0.0001 -0.0002 -0.0033 -0.0013

[0.282] [0.297] [0.117] [0.070]PIPERC -0.0002 -0.0003 -0.0001 -0.0002

[0.086] [0.283] [0.463] [0.323]LOGTNA 0.0002 -0.0028 -0.0127 -0.0012

[0.045] [0.000] [0.000] [0.016]MULTCAT 0.0000 -0.0001 0.0019 0.0010

[0.476] [0.484] [0.130] [0.040]LAGOTHER 0.0003 0.0011 0.0002 0.0003

[0.021] [0.041] [0.247] [0.343]

Left Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 0.1073 -0.0175 0.0100 0.0373

[0.000] [0.387] [0.485] [0.013]|yi,t−1 − yt−1 < 0| 0.0699 0.0390 -0.0173 0.0700

[0.000] [0.150] [0.319] [0.010]AV GY IELD -0.0191 0.1573 0.2337 0.0575

[0.360] [0.082] [0.097] [0.169]EXPR -0.1978 -0.3824 -0.1960 -0.3044

[0.015] [0.013] [0.218] [0.000]FLOWSTDEV 0.5108 0.4688 0.4026 0.3201

[0.000] [0.001] [0.001] [0.000]PIPERC 0.0525 0.1207 0.3097 0.0760

[0.108] [0.141] [0.023] [0.192]LOGTNA -0.0069 0.1575 0.1259 -0.1222

[0.639] [0.168] [0.105] [0.047]MULTCAT -0.0265 -0.1041 0.0487 -0.0900

[0.213] [0.120] [0.405] [0.066]LAGOTHER 0.0212 -0.0193 -0.2457 -0.0094

[0.300] [0.336] [0.035] [0.359]

Right Tail ExposureVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 0.0120 0.1204 -0.0163 0.0500

[0.136] [0.093] [0.313] [0.066]|yi,t−1 − yt−1 < 0| 0.0599 0.0660 0.0544 0.0516

[0.000] [0.073] [0.149] [0.004]AV GY IELD -0.0215 0.0016 0.1365 0.0075

[0.310] [0.476] [0.139] [0.517]EXPR -0.2006 -0.0836 -0.0371 -0.1502

[0.001] [0.148] [0.156] [0.031]FLOWSTDEV 0.6445 0.5120 0.4280 0.4628

[0.000] [0.002] [0.011] [0.000]PIPERC 0.0749 0.1282 0.0312 0.0402

[0.034] [0.079] [0.442] [0.524]LOGTNA 0.0532 0.0672 0.2399 -0.0525

[0.156] [0.261] [0.071] [0.325]MULTCAT -0.0700 -0.0586 -0.0824 -0.0730

[0.078] [0.313] [0.154] [0.089]LAGOTHER -0.0005 -0.1395 0.1350 0.0397

[0.427] [0.273] [0.327] [0.255]

Table 9: Fund-Level Panel Quantile Regression - Prime Institutional with Cross-Category Variables

This table presents the coefficients from estimating equation (2). β0, β1, and β2 vary over three different periods

in 2008: 9/10-9/16, 9/17-9/19, and 9/22-10/17, respectively. Time dummies are omitted for brevity. Numbers in

brackets are one-sided bootstrapped p-values clustered at the fund level. Coefficients for lagged variables have been

divided by 100 for spacings 1 and 2.

Prime Institutional FundsVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 -0.0444 0.3191 0.2234 -0.0393

[0.001] [0.015] [0.114] [0.173]yi,t−1 − yt−1 < 0 -0.0175 0.2759 0.0519 0.1037

[0.346] [0.072] [0.421] [0.007]AV GY IELD 0.0001 -0.0012 -0.0120 -0.0006

[0.303] [0.124] [0.004] [0.246]EXPR -0.0001 0.0012 0.0083 0.0016

[0.306] [0.243] [0.017] [0.028]FLOWSTDEV 0.0013 -0.0027 -0.0039 0.0001

[0.000] [0.058] [0.148] [0.457]PIPERC -0.0002 -0.0028 -0.0066 -0.0007

[0.075] [0.087] [0.087] [0.158]LOGTNA 0.0005 -0.0060 -0.0214 -0.0011

[0.002] [0.000] [0.000] [0.127]MULTCAT 0.0000 0.0004 -0.0012 0.0005

[0.565] [0.359] [0.407] [0.234]LAGOTHER 0.0002 0.0013 0.0056 0.0003

[0.048] [0.099] [0.014] [0.278]

Prime Retail FundsVariable (Baseline) (Pre-Crisis) (Crisis) (Post-Crisis)yi,t−1 − yt−1 > 0 -0.4281 -0.5716 -0.3848 -0.2849

[0.000] [0.052] [0.109] [0.032]yi,t−1 − yt−1 < 0 -0.1073 -0.2192 -0.5260 -0.1355

[0.099] [0.185] [0.302] [0.120]AV GY IELD 0.0004 0.0019 0.0003 -0.0002

[0.092] [0.241] [0.505] [0.346]EXPR -0.0002 -0.0002 0.0011 0.0007

[0.163] [0.476] [0.423] [0.264]FLOWSTDEV 0.0034 0.0048 0.0012 0.0009

[0.001] [0.133] [0.497] [0.269]PIPERC -0.0003 0.0012 0.0054 0.0011

[0.114] [0.433] [0.234] [0.100]LOGTNA -0.0004 -0.0030 -0.0080 -0.0024

[0.245] [0.160] [0.007] [0.000]MULTCAT 0.0004 0.0003 0.0020 -0.0010

[0.107] [0.494] [0.276] [0.114]LAGOTHER -0.0004 -0.0019 -0.0070 -0.0016

[0.290] [0.148] [0.130] [0.069]

Table 10: Fund-Level Panel Regressions - OLS Coefficients with Cross-Category Variables

This table presents the coefficients from an OLS regression with time dummies, where the slope coefficients vary over

three different periods in 2008: 9/10-9/16, 9/17-9/19, and 9/22-10/17, respectively. Time dummies are omitted for

brevity. Numbers in brackets are one-sided bootstrapped p-values clustered at the fund level.

 

Figure

e 1: Daily F Surroun

Flows to/fromnding the Cr

47

m the Reservrisis Week (i

ve Primary Fin %)

Fund Duringg the Period

Figure 1: Daily Flows to/from the Reserve Primary Fund in September 2008 (in %)

 

Figure 2:

Daily FlowsDuring the P

48

s to/from MoPeriod Surro

oney Fund Counding the

Categories Crisis Weekk (in $Billionns)

Figure 2: Daily Flows to/from Money Fund Categories in September 2008 (in $Billions)

09/01 09/06 09/11 09/16 09/21 09/26 10/01 10/06 10/11 10/16 10/21 10/26−0.2

−0.1

0

0.1

Net

Flo

w

Quantiles of Prime Institutional Flows: September−October 2008

Q10

Q50

Q90

09/01 09/06 09/11 09/16 09/21 09/26 10/01 10/06 10/11 10/16 10/21 10/26−0.05

0

0.05

Net

Flo

w

Quantiles of Prime Retail Flows: September−October 2008

Q10

Q50

Q90

Figure 3: Quantiles of Daily Flow Distributions by Category in September-October 2008 (in %)

This figure plots the 10th, 50th, and 90th quantiles of complex-category level flows (as afraction of fund assets) for September-October 2008. The top panel corresponds with PrimeInstitutional funds, and the bottom panel corresponds with Prime Retail funds.

02/0103/01 04/01 05/01 06/01 07/01 08/01 09/01 10/01

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6Inst: Feb−Oct 2008

Co

effi

cien

t

Date

09/01 09/11 09/21 10/01 10/11 10/21

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6Inst: Crisis Period

Co

effi

cien

t

Date

02/0103/01 04/01 05/01 06/01 07/01 08/01 09/01 10/01

−0.8

−0.6

−0.4

−0.2

0

0.2Retail: Feb−Oct 2008

Co

effi

cien

t

Date

09/01 09/11 09/21 10/01 10/11 10/21

−0.8

−0.6

−0.4

−0.2

0

0.2Retail: Crisis Period

Co

effi

cien

t

Date

Figure 4: Rolling AR(1) Coefficients for Daily Fund-Level Log Flows

In this figure, we plot coefficient estimates based on panel regressions of the form

yit = αt + φyit−1 + εit,

where yit = (TNAit−TNAit−1)/TNAit−1 is the estimated flow rate for fund i on day t. Thepersistence measure, φ, is obtained using a five-day rolling estimation window. 95% confidenceintervals using robust standard errors are given by lighter lines. The top panel shows thecoefficient estimates for Prime Institutional funds, while the bottom panel corresponds withPrime Retail funds. The right panel focuses on September-October 2008.

09/06 09/11 09/16 09/21 09/26 10/01 10/06−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

Net

flo

w

Institutional − Lagged Flow Tercile 1

Q10

Q50

Q90

09/06 09/11 09/16 09/21 09/26 10/01 10/06

−0.1

−0.05

0

0.05

0.1

0.15

Net

flo

w

Retail − Lagged Flow Tercile 1

09/06 09/11 09/16 09/21 09/26 10/01 10/06−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

Net

flo

w

Institutional − Lagged Flow Tercile 2

09/06 09/11 09/16 09/21 09/26 10/01 10/06

−0.1

−0.05

0

0.05

0.1

0.15

Net

flo

w

Retail − Lagged Flow Tercile 2

09/06 09/11 09/16 09/21 09/26 10/01 10/06−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

Net

flo

w

Institutional − Lagged Flow Tercile 3

09/06 09/11 09/16 09/21 09/26 10/01 10/06

−0.1

−0.05

0

0.05

0.1

0.15

Net

flo

w

Retail − Lagged Flow Tercile 3

Figure 5: Quantiles of the Weekly Outflow Distribution for Terciles Formed on Lagged Outflows (in %)

For each date, funds are sorted into terciles according to lagged flows (as a % of fund assets).We report the 10th, 50th, and 90th quantiles of the subsequent 1-week flow distribution withineach tercile. The left column corresponds to Prime Institutional funds, and the right columncorresponds to Prime Retail funds

09/06 09/11 09/16 09/21 09/26 10/01 10/060

0.2

0.4

0.6

0.8

Tra

nsi

tio

n p

rob

abil

ity

Institutional − Lagged Flow Tercile 1

T1

T3

09/06 09/11 09/16 09/21 09/26 10/01 10/060

0.2

0.4

0.6

0.8Retail − Lagged Flow Tercile 1

09/06 09/11 09/16 09/21 09/26 10/01 10/060

0.2

0.4

0.6

0.8

Tra

nsi

tion

pro

bab

ilit

y

Ranking date

Institutional − Lagged Flow Tercile 3

09/06 09/11 09/16 09/21 09/26 10/01 10/060

0.2

0.4

0.6

0.8

Ranking date

Retail − Lagged Flow Tercile 3

Figure 6: Probability of a Fund Being in the Top/Bottom Tercile of the Weekly Flow DistributionGiven Tercile of Previous Day Flows During the Period Surrounding the Crisis Week

For each date, funds are sorted into terciles according to lagged flows (as a % of fund assets).For each bin, we report the fraction of funds which fall into the bottom/top tercile when rankedby subsequent 1-week flows. The left column corresponds to Prime Institutional funds, andthe right column corresponds to Prime Retail funds

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

AVGYIELD Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

AVGYIELD Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

EXPR Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

EXPR Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

FLOWSTDEV Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

FLOWSTDEV Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

PIPERC Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

PIPERC Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

LOGTNA Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.6

−0.4

−0.2

0

0.2

LOGTNA Top Tercile

Figure 7: Quantiles of Fund-level Cumulative Flows for Terciles of Funds Ranked by Characteristics -Prime Institutional Funds During the Period Surrounding the Crisis Week (in %)

For each date, funds are sorted into terciles based on a given characteristic. For the topand bottom terciles, we report the 10th, 50th, and 90th quantiles of the cumulative flowdistributions (solid lines) as a percentage of initial period assets. Equal-weighted averages aregiven by dashed lines. Horizontal axis plots the day in September 2008.

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1AVGYIELD Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1AVGYIELD Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1EXPR Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1EXPR Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1FLOWSTDEV Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1FLOWSTDEV Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1PIPERC Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1PIPERC Top Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1LOGTNA Bottom Tercile

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.2

−0.1

0

0.1LOGTNA Top Tercile

Figure 8: Quantiles of Fund-level Cumulative Flows for Terciles of Funds Ranked by Characteristics -Prime Retail Funds During the Period Surrounding the Crisis Week (in %)

For each date, funds are sorted into terciles based on a given characteristic. For the topand bottom terciles, we report the 10th, 50th, and 90th quantiles of the cumulative flowdistributions (solid lines) as a percentage of initial period assets. Equal-weighted averages aregiven by dashed lines. Horizontal axis plots the day in September 2008.

09/11 09/16 09/21 09/26 10/01 10/06 10/11 10/16−0.1

−0.08

−0.06

−0.04

−0.02

0

0.02

0.04

Date

Log F

low

Institutional − Common Shocks

OLS

QREG

09/11 09/16 09/21 09/26 10/01 10/06 10/11 10/160

0.02

0.04

0.06

0.08

0.1

Date

Log F

low

Institutional − Common Shocks to Tails

Left Tail

Right Tail

09/11 09/16 09/21 09/26 10/01 10/06 10/11 10/16−0.02

−0.01

0

0.01

0.02

Date

Log F

low

Retail − Common Shocks

09/11 09/16 09/21 09/26 10/01 10/06 10/11 10/160

0.01

0.02

0.03

0.04

0.05

Date

Log F

low

Retail − Common Shocks to Tails

Figure 9: Common shocks from panel regressions

This figure plots the shocks (i.e. time dummies) from the panel OLS and quantile regressions inTables 5-7. The left panel shows the shocks which affect the central location of the distributionof fund flows, ξ0,t, for the OLS regressions and quantile regressions, respectively. The rightpanel shows the common shocks to the left and right tails, exp(ξ1,t) and exp(ξ2,t), from thequantile regression models in Tables 6-7, which have no analog in the OLS model. The shockto the left tail, exp(ξ1,t), is normalized so that it equals distance between the median andthe 10th percentile of the flow distribution for a fund which had a lagged flow equal to thecategory average and other variables equal to the cross-sectional mean for funds in the samecategory. The shock to the right tail, exp(ξ2,t), is normalized to equal the distance betweenthe 90th percentile and the median for a fund with the same characteristics.

2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4−0.25

−0.2

−0.15

−0.1

−0.05

0

0.05

Average Gross Yield: March−August 2008

Log F

low

on 9

/17/2

008

Level Curves of Conditional Quantile Functions for Log Flows on 9/17/2008

Cond Quantiles

90% Conf Intervals

−0.14 −0.12 −0.1 −0.08 −0.06 −0.04 −0.02 0 0.02 0.040

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Log Flow on 9/17/2008

Pro

bab

ilit

y

Distributions of Log Flows on 9/17/2008

Yield at Mean

Yield at Mean + 1 Std Dev

Quantile Locations: Yield at Mean

Quantile Locations: Yield at Mean + 1 Std Dev

Figure 10: Marginal Effects of Average Yield Variable on Log Flow Distribution for 9/17/2008 - PrimeInstitutional Funds

The top panel plots the effect of changing the variable AV GY IELD on the 10th, 50th, and90th conditional quantiles of the flow distribution for Prime Institutional funds on 9/17/2008.Lagged flows are held constant at the category-level flow and all other variables are heldconstant at their cross-sectional averages. 90% bootstrapped confidence intervals are plot-ted by light dashed lines. Stars mark the location of the cross-sectional average value ofAV GY IELD (about 2.95% per annum), and triangles add 1 standard deviation (about 15bp) to this value. The bottom panel plots the conditional distribution functions of flows atthese two points, where we interpolate between quantiles by assuming that ηi,t, as definedin the text, is drawn from an exponential distribution. This distributional assumption is notimposed in the estimation procedure. As above, stars and triangles (along with dashed lines)mark the locations of the conditional quantiles for each value of AV GY IELD.

5 10 15 20 25−0.15

−0.1

−0.05

0

0.05

0.1

Net flows: yi,t−1

−y−i,t−1

= −0.1

Net

flo

ws

Event Date

5 10 15 20 25−0.15

−0.1

−0.05

0

0.05

0.1

Net flows: yi,t−1

−y−i,t−1

= 0

Event Date

5 10 15 20 25−0.15

−0.1

−0.05

0

0.05

0.1

Net flows: yi,t−1

−y−i,t−1

= 0.1

Event Date

Figure 11: Impact of lagged flows for Prime Institutional funds in baseline specification

The top panel plots the effect of changing yi,t−1− yt−1 on the 10th, 50th, and 90th conditionalquantiles of the flow distribution for Prime Institutional funds for each date in our sampleperiod. All other variables are held constant at their cross-sectional averages. The picturefrom the center panel, where yi,t−1 = yt−1, is superimposed in the left and right panels inlight gray for ease of comparison.

−4 −3 −2 −1 0 1 2 3 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Pro

bab

ilit

y d

ensi

ty

Standardized residual

K−S Density

Laplace

Figure 12: Standardized residual density from baseline model - Institutional funds

This figure plots the empirical distribution of the standardized residuals from the model inTable 6, ηi,t, which is generated using a kernel smoother. The dashed line plots the density ofa Laplace-distributed random variable which has been normalized to satisfy the conditionalquantile restriction which is assumed when estimating the model.

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

AVGYIELD − 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

AVGYIELD + 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

EXPR − 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

EXPR + 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

FLOWSTDEV − 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

FLOWSTDEV + 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

PIPERC − 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

PIPERC + 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

LOGTNA − 1 STDEV

10 11 12 15 16 17 18 19 22 23 24 25 26

−0.4

−0.2

0

LOGTNA + 1 STDEV

Figure 13: Impact of explanatory variables on multi-period flow distributions - Institutional funds

This figure shows the impact of explanatory variables on quantiles of multi-period cumulativeflow distributions (as a % of initial assets). The lines plot the 10th, 50th, and 90th quantilesof the cumulative flow distributions, respectively. We fix each of the explanatory variablesat its average plus or minus one standard deviation. The initial value of lagged flows isassumed to be equal to the category average, and all other variables are fixed at their averages.We estimate the quantiles by drawing shocks from the Laplace distribution and using therecursive definition of the dependent variable to simulate 10,000 sample paths for each set ofconditioning variables.